Anyone that knows me knows that I love potatoes..any way I can get them. I found this and stopped dead in my tracks...honestly, I am in heaven right now! What is not to love about this sandwich?!
My new Breakfast sandwich for sure..add a fried egg and you DO have breakfast!
In a fry pan heat enough canola oil to lightly cover bottom of pan and place potato slices to cover bottom of pan..salt and pepper. Cook until tender and browned nicely. Then drain potatoes on paper towel and sprinkle with a pinch of garlic powder.
While potatoes are cooking, cook bacon until crispy and drain well on paper towel. You could use Canadian bacon..fry until warmed and browned on each side before adding to sandwich.
I used a heavy saucepan to place on top of the sandwich when grilling the second side..helps to press the sandwich together nicely..


text:
Third-Half-Birthday
By Courtney Espejo - December 18, 2012
Grace face is officially two and a half!
This little girl is growing up with the biggest personality.
She definitely keeps us on our toes and keeps us laughing ALL the time.
She says the funniest things, and I often find myself looking over to Isaac to see if he caught it, too.
She won't go to bed unless Mommy is there to tickle her arm or back. The other night she told me she had to roll her "weebs" up, so I could tickle her arm better. If she's wearing a onesie, she'll fight me until I let her unzip it "just a wittle bit, mom, so you can tickow back." How can I say no to that?!
Gracie also loves to snack. She is the slowest eater ever, and since we go out to dinner so often, we end up waiting for her to finish. She never does and will request a pouch or two as soon as we get into bed. Luckily, we ventured away from the Elmo pouches, and she's now eating stuff called "blueberry, pear, purple carrot" or "carrots, apples, and parsnips". They use only organic fruits and veggies, so I let her eat them at her request.
Her other favorite foods are chicken fingers, macaroni and cheese, peanut butter and jellies, and chicken pot pies.
She's weighing in at 32 pounds and is 35 and a half inches tall.
She now officially wears size 3T in shirts as she can't fit into many of her 2T clothes. The sleeves are too short to cover her little arms. She can still fit into her 2T pants, though, because girl is a shorty (near the fiftieth percentile). I've still been buying them in 3T sizes so they'll last a whole year.
She wears size 7 in shoes and probably has more than most two year olds. She loves to shop. We took her into Janie and Jack the other day, and they had a basket of shoes on the ground. Grace made Isaac come look, excitedly telling him, "Oh my gosh! Look at all of these cute little booties they have."
She is still a movie lover, too. We're currently watching a lot of Tinkerbell. Grammy has her all into fairies at the moment. She also loves Finding Nemo (which she just calls "Emo").
Baby girl still loves blue, and wants everything to be her favorite color.
Can Santa use blue wrapping paper? I want blue shoes! Can you make blue frosting?
Her hair is super long...almost to her tush:
That also happens to be her most recent nickname. I call Grace "tush" and Charley "little tush". Poor kids...I just can't get enough of their squishy little tushies!
Grace loves going to school. They moved her into the early preschool room part time, because she's so smart! One day she came home counting to ten in Spanish. This girl totally amazes me.
We're still going to swim lessons, and she is doing great. I'm hoping in another couple of months she'll be a pro, and we can start gymnastics instead.
She is still a big help with Charley. When baby sister cries, Grace tells her it's ok. When she's sleeping, Grace likes to rub her little head and hold her hand. When she has her binky in, Grace takes it because "she wants to talk, Mom". She even tries to share her dinner with her when we're out to eat. I have to keep reminding her that Charley isn't ready for big girl food yet. Grace's response, "Oh she's yittow tiny, huh, Mom?"
Don't get me wrong. They do have typical sister moments. Just the other day, Grace was playing her drums as Charley sat nearby watching. Grace got up, shoved Charley down, and went right back to her drums. That one earned her a time-out. She isn't shy about telling on herself either. She'll come in the room and say, "I pushed Chawdey on her head".
Grace absolutely loves music, and yells at Isaac or I if we turn it down in the car. There is always some song running through her head. For example, yesterday she asked for a juice box, and when I handed it to her, she turned and ran towards the other room belting out "in a one horse open sleigh".
Her favorite song is currently We Are Never Ever Getting Back Together by Taylor Swift. She knows almost every word and asks for it by name. She also loves Scream and Shout by the one and only Britney Spears. Instead of "I want to scream and shout and let it all out", Grace says "I wanna scweam and shout and let it go now". She's the cutest!
When she's thirsty so tells us, "I'm sooo drinky!"
When I tell her she can't have something she says, "Just only one, and that's it."
When I tell her to wait for something, she says, "just a minute and then we can have it?!"
This little girl just melts my heart. I can't imagine a day without her in it, and it terrifies me to think of her getting big and starting school in a couple of years. I wish I could just keep her small forever.
Mommy and Daddy love you more than you'll ever know. You're our little sunshine and you make us smile no matter what is going on in our busy little life.
Happy Third-Half-Birthday, Grace Face <3
Grace Milestones
Mommy's Girl
Snow Bunnies
Happy Birthday, Jesus
Christmas To-Do List

redpajama_set_name: RedPajamaCommonCrawl


gorilla decking complaints - Composite Decking Gorilla deck vinyl decking - Gorilla deck is the future of decking. Great Railing provides Quality Decking and Fencing plus a whole lot more.
Aluminum Decking Price Guide - Home Remodeling The average aluminum decking cost is more than wood, but these decks offer plenty of benefits. See all aluminum decking material and installation prices. Then, find a local decking contractor for your deck installation via ImproveNet.
price of gorilla decking Decking The decking has passed the ASTM B117 salt fog spray test on new ACQ treated lumber. . Initially, the purchase price of vinyl is slightly higher than wood, but it will save you money long term due to: Virtually no . Vekadeck and Gorilla Deck.
gorilla decking problems - Outdoor Deck Price gorilla decking complaints - Composite Decking Price,Gorilla Deck Decking Reviews - Decks.com Gorilla Deck Vinyl Decking is manufactured by Homeland Vinyl Products in Birmingham, AL.
pricing for gorilla decking Decking The decking has passed the ASTM B117 salt fog spray test on new ACQ treated lumber. Initially, the purchase price of vinyl is slightly higher than wood, but it will save you money long term due to: Virtually no Vekadeck and Gorilla Deck.
Gorilla Decking Al-Mar Vinyl and Aluminum Exeter Ontario Gorilla Deck will never rot, split, or decay, and is impervious to insects and moisture. We aren't saying its invincible, but its pretty close Gorilla Decking Lifetime of beauty Gorilla Deck will never rot, split, or decay, and is impervious to insects and moisture.


4WD Treks Close to Melbourne is an indispensable companion to twenty of the best four-wheel drive destinations within a couple of hours drive from the city. This book features day trips as well as weekend adventures to secluded and long forgotten gold mining towns, journeys through pristine areas of lush rainforest and sojourns to the top of remote mountain peaks with panoramas that stretch for kilometres.
With comprehensive and easy to follow route directions, 4WD Treks Close to Melbourne is your passport to some of the best picnic, walking, camping, swimming, fishing and four-wheel driving destinations on Melbournes's doorstep. Route directions read in forward and reverse as well as featuring both GPS Lat/Long and UTM Grid References.


text:
A mammalian somatic "cell cycle" mutant defective in G1
R. Michael Liskay
Molecular & Medical Genetics
Variants or "mutants" temperature‐sensitive (ts) for growth have been isolated by selection from a near‐diploid mouse cell line. Thus far. 10 ts mutants which grow normally at 33° C, but not at 39° C, have been isolated. These ts mutants were then studied to determine if any manifested their defect at a unique point or stage in the cell cycle. This type of ts mutant is termed a "cell cycle" mutant. The first screen involves observing individual cells of an asynchronous culture for residual division after a shift from 33° C (permissive temperature) to 39° (nonpermissive temperature). A cell cycle mutant should show some fraction of the cells dividing only once at a normal rate after the shift. The ts variant B54 met this first criterion for a cell cycle mutant (i.e., 50% residual division) and was further analyzed. The second screening technique monitors (1) the rate of entry into S, (2) the length of G2, and (3) the rate and duration of cells entering mitosis after a shift of an asynchronous culture to 39°. This experiment with B54 revealed that cells in G1 at the time of the shift to 39° failed to enter S while cells already into S completed the cycle at 39°. These results suggest that B54 is defective in a G1 function which is required for entry into S, but which is no longer needed once cells have entered S. Other results are presented which also support this hypothesis. In addition the ts function of B54 is apparently required for recovery from a "high density" G1 arrest.
Journal of Cellular Physiology
https://doi.org/10.1002/jcp.1040840106
10.1002/jcp.1040840106
Dive into the research topics of 'A mammalian somatic "cell cycle" mutant defective in G1'. Together they form a unique fingerprint.
Cell Cycle Medicine & Life Sciences 100%
Length Chemical Compounds 75%
Time Chemical Compounds 45%
Temperature Medicine & Life Sciences 36%
Mitosis Medicine & Life Sciences 25%
Cell Culture Techniques Medicine & Life Sciences 19%
Cell Line Medicine & Life Sciences 15%
Growth Medicine & Life Sciences 12%
Michael Liskay, R. (1974). A mammalian somatic "cell cycle" mutant defective in G1. Journal of Cellular Physiology, 84(1), 49-55. https://doi.org/10.1002/jcp.1040840106
A mammalian somatic "cell cycle" mutant defective in G1. / Michael Liskay, R.
In: Journal of Cellular Physiology, Vol. 84, No. 1, 08.1974, p. 49-55.
Michael Liskay, R 1974, 'A mammalian somatic "cell cycle" mutant defective in G1', Journal of Cellular Physiology, vol. 84, no. 1, pp. 49-55. https://doi.org/10.1002/jcp.1040840106
Michael Liskay R. A mammalian somatic "cell cycle" mutant defective in G1. Journal of Cellular Physiology. 1974 Aug;84(1):49-55. doi: 10.1002/jcp.1040840106
Michael Liskay, R. / A mammalian somatic "cell cycle" mutant defective in G1. In: Journal of Cellular Physiology. 1974 ; Vol. 84, No. 1. pp. 49-55.
@article{e05e89439e684713b72da1b5f4f78345,
title = "A mammalian somatic "cell cycle" mutant defective in G1",
abstract = "Variants or "mutants" temperature‐sensitive (ts) for growth have been isolated by selection from a near‐diploid mouse cell line. Thus far. 10 ts mutants which grow normally at 33° C, but not at 39° C, have been isolated. These ts mutants were then studied to determine if any manifested their defect at a unique point or stage in the cell cycle. This type of ts mutant is termed a "cell cycle" mutant. The first screen involves observing individual cells of an asynchronous culture for residual division after a shift from 33° C (permissive temperature) to 39° (nonpermissive temperature). A cell cycle mutant should show some fraction of the cells dividing only once at a normal rate after the shift. The ts variant B54 met this first criterion for a cell cycle mutant (i.e., 50% residual division) and was further analyzed. The second screening technique monitors (1) the rate of entry into S, (2) the length of G2, and (3) the rate and duration of cells entering mitosis after a shift of an asynchronous culture to 39°. This experiment with B54 revealed that cells in G1 at the time of the shift to 39° failed to enter S while cells already into S completed the cycle at 39°. These results suggest that B54 is defective in a G1 function which is required for entry into S, but which is no longer needed once cells have entered S. Other results are presented which also support this hypothesis. In addition the ts function of B54 is apparently required for recovery from a "high density" G1 arrest.",
author = "{Michael Liskay}, R.",
doi = "10.1002/jcp.1040840106",
journal = "Journal of Cellular Physiology",
publisher = "Wiley-Liss Inc.",
T1 - A mammalian somatic "cell cycle" mutant defective in G1
AU - Michael Liskay, R.
N2 - Variants or "mutants" temperature‐sensitive (ts) for growth have been isolated by selection from a near‐diploid mouse cell line. Thus far. 10 ts mutants which grow normally at 33° C, but not at 39° C, have been isolated. These ts mutants were then studied to determine if any manifested their defect at a unique point or stage in the cell cycle. This type of ts mutant is termed a "cell cycle" mutant. The first screen involves observing individual cells of an asynchronous culture for residual division after a shift from 33° C (permissive temperature) to 39° (nonpermissive temperature). A cell cycle mutant should show some fraction of the cells dividing only once at a normal rate after the shift. The ts variant B54 met this first criterion for a cell cycle mutant (i.e., 50% residual division) and was further analyzed. The second screening technique monitors (1) the rate of entry into S, (2) the length of G2, and (3) the rate and duration of cells entering mitosis after a shift of an asynchronous culture to 39°. This experiment with B54 revealed that cells in G1 at the time of the shift to 39° failed to enter S while cells already into S completed the cycle at 39°. These results suggest that B54 is defective in a G1 function which is required for entry into S, but which is no longer needed once cells have entered S. Other results are presented which also support this hypothesis. In addition the ts function of B54 is apparently required for recovery from a "high density" G1 arrest.
AB - Variants or "mutants" temperature‐sensitive (ts) for growth have been isolated by selection from a near‐diploid mouse cell line. Thus far. 10 ts mutants which grow normally at 33° C, but not at 39° C, have been isolated. These ts mutants were then studied to determine if any manifested their defect at a unique point or stage in the cell cycle. This type of ts mutant is termed a "cell cycle" mutant. The first screen involves observing individual cells of an asynchronous culture for residual division after a shift from 33° C (permissive temperature) to 39° (nonpermissive temperature). A cell cycle mutant should show some fraction of the cells dividing only once at a normal rate after the shift. The ts variant B54 met this first criterion for a cell cycle mutant (i.e., 50% residual division) and was further analyzed. The second screening technique monitors (1) the rate of entry into S, (2) the length of G2, and (3) the rate and duration of cells entering mitosis after a shift of an asynchronous culture to 39°. This experiment with B54 revealed that cells in G1 at the time of the shift to 39° failed to enter S while cells already into S completed the cycle at 39°. These results suggest that B54 is defective in a G1 function which is required for entry into S, but which is no longer needed once cells have entered S. Other results are presented which also support this hypothesis. In addition the ts function of B54 is apparently required for recovery from a "high density" G1 arrest.
U2 - 10.1002/jcp.1040840106
DO - 10.1002/jcp.1040840106
JO - Journal of Cellular Physiology
JF - Journal of Cellular Physiology


You may know someone who has been stopped for a DUI. You may also know that the consequences could be drastic. The reason for this is that Arizona and all of the other states have DUI laws that were created to stop drivers from getting behind the wheel of a car after they have been drinking or consuming any substance that may impair driving abilities. The limit for alcohol related consumption in Arizona also referred to the "legal limit," is .08%.
The best advice any law firm can offer is "do not drink and drive under any circumstance". There is no excuse and the costs are incredibly high if you are caught and convicted. DUI fines and attorneys' fees could be in the tens of thousands of dollars. And this does not even take into account the jail time and costs associated with loss of work, loss of license, and other related expenses. All of that being said, DUI stops do happen and the second best thing (after simply not drinking and driving) is to know exactly how to act if you are pulled over under suspicion of driving while intoxicated.
Here are six important things to know if you happen to get pulled over.
1) Stay in your car unless you are specifically asked to step out. Additionally, if you have a seat belt on, leave it on!
2) You will be asked for your driver's license and registration. Always have these items readily available in your vehicle. How easily you are able to find these items will almost always be noted on the officer's report. If you cannot find them, or you fumble around in your care looking for them, it could look like you have had too much to drink.
3) If asked, firmly but politely refuse to take any and all field tests. Such field sobriety assessments are: walking in a straight line, touching a finger to your nose, saying ABCs backward, holding a leg up while counting, or following a light with your eyes. Be aware that any time you participate in field testing; you are potentially giving evidence that will be used against you in your DUI case. There are no laws that require you to do any of these tests. Some officers will tell you they will take you to jail if you don't do the tests. This is not legally true and could be challenged if you end up going to court. They were going to take you to jail anyway, so it is best to calmly let them take you, but NEVER do these tests.
4) Never allow a search of your vehicle. Many times, the officer that stops you will ask you to agree to a search of your vehicle. You have the right to say no if this is asked of you. If an officer has enough reason to get a search warrant, he or she will. If not, then why search? Simply say no if you are asked! But always be polite and never feel as though you must explain why.
5) Courteously refuse to answer any additional questions. When officers stop drivers, they will ask questions about what you have had to drink and how much you have had. The best way to respond to this question is: "I can only answer that questions with the advice of my attorney." Please note that you do not have to call or even have attorney at that exact moment. However, such a statement usually stops any further questions by invoking your constitutional rights, If and when the officer reads your Miranda rights, the answer should always be exactly the same.
6) Always cooperate in any way that you can. Cooperation means having a pleasant and calm attitude, as well as being polite. You do not need to answer questions, complete field tests, or even talk. But you should note that your attitude, appearance, and anything you say could become part of the officer's report. This report could be used against you, so make sure you are extremely careful. By no means should you ever crack jokes, cry, apologize to the officer, admit any wrongdoing, or confess.
Drivers in the Tempe, Arizona area and beyond have the Weingart Firm to provide the services they need for all kinds of drunk driving cases. If you or someone you know has been charged with a DUI, now is the time to act. Our aggressive and skilled team of attorneys have many years of experience in DUI law, and will work to help you fix your situation as soon as possible. Call our offices today to schedule a free case review.


text:
Mason Takes on Korea
Tijani Musa poses in front of welcome sign at Mason Korea.
This story was originally published in the Feb. 23 issue of Fourth Estate.
Jevette Brown, Staff Writer
With a reputation for innovation to uphold, Mason continues to make major strides forward that prove we deserve our top five position in "Up-and-Coming Institutions" as reported by U.S. News and World Report's annual survey. One of these most recent opportunities that the university is offering, is the new campus location in Korea.
Less than a year ago, Mason opened the doors of its first international campus in Songdo, Korea. Located 25 miles from the capital city of Seoul, the school already has around 2,000 students attending. It has availability to state-of-the-art technology as well as brand new dorms and faculty housing. Degree programs are offered in Economics and Management, and according to the Mason Korea official website, the staff is made up of a combination of "tenured, term or tenured-track professors" from Mason in the United States as well as "qualified Korean faculty."
The students attending Mason Korea are required to spend their junior year in the United States and U.S. students are also welcomed to spend a semester or two abroad in Korea.
Senior and Global Affairs major, Tijani Musa, just returned from spending his Fall 2014 semester in Songdo,. He decided to pursue this opportunity not only to learn about Korean culture, but also with the values of Mason in mind.
"It's essential for us to know other cultures to bridge that gap so that when students or colleagues display any aspect of their culture here it is important that we don't appear to judgmental" Musa said.
Musa was given the full South Korean experience while in Songdo. For example, kimchi (or kim-chee), a popular Korean vegetable dish, and white rice became parts of everyday meals for him. Also, as a practicing Muslim who doesn't eat pork, the meat became one of the first words that Musa had to learn because of its common usage in Korean dishes.
Having never studied the language, trying to communicate effectively at Mason Korea was a difficulty Musa faced. Since coming to the United States as a young teenager from Sierra Leone, Africa, he hadn't battled such strong language barriers in a long time.
"Ordering a cookie or candy became one of the most difficult things I had to do. Something that was second nature to me here became one of the most difficult things I experienced there," Musa said.
Luckily, a professor at Mason Korea helped Musa out and offered to let him take her class for free so he was able to catch on a little quicker.
This opportunity he was provided by the professor might have been due to the small size of the classes, with maybe 5-7 people in each course; a huge contrast to the some of the 100+ student size lectures, here at the Fairfax campus. According to Musa, while this smaller class size did require increased participation from students as well as more professor availability, these classes were also "un-skippable" because the professor remembered everyone names and faces easily.
However, one of the advantages to not being a substantially sized university was the opportunity that the students received to mingle with some faces we don't get to see on a personal level here. For example, Musa recalls when Mason's President Cabrera and Senior Vice President for Administration and Finance, J.J. Davis came to talk to the students.
"You get to interact with them because the population of students is so small. You can see them in the hallways. It was a nice feeling," Davis said.
Conversing with President Cabrera wasn't Musa's only great memory. He played on the Mason Intramural Basketball team that won the championship between the other universities on campus (SUNY, Ghent University from Belgium, University of Utah and two nearby Korean universities, Yonsei and Incheon). He also helped create 6 new clubs and visited the city that was the inspiration behind PSY's catchy song "Gangnam Style." Musa even had his first experience as a DJ at a"Winter Wonderland" party he and his coworkers hosted with the students to celebrate the end of finals.
Musa credits a lot of his opportunity to have this adventure towards the man he calls his "recruiter," James Burke, the Events and Outreach Director in the Office of Global Strategy. Burke is one of the many people behind the Mason Korea Study Abroad program. He speaks highly of Musa, proud of the many things he accomplished over just one semester in Songdo and Musa advertises for Burke in return; highly recommending the experience to anyone thinking about traveling abroad.
"In order to expand yourself you have to travel. It makes you develop as an individual and as a young professional. I would definitely go back if I could. My mind is expanded now," Musa said.
experience gmu abroad issue 16 korea school abroad south korea student traveling
About Savannah Norton
Savannah is the Lifestyle Editor of Fourth Estate. She is a senior majoring in communications and minoring in Journalism. She one day hopes to be the next Giuliana Rancic. This past school year, she interned at FOX5 news in D.C. &Did we mention that she knows every word to every Taylor Swift song? twitter: @savvygirrl
View all posts by Savannah Norton →
A look at the making of "In the Next Room"
Mason's Flexible Housing program to be reviewed
DATING DURING A PANDEMIC


text:
export class Component {
  static create = jest.fn();

  get = jest.fn();
}

redpajama_set_name: RedPajamaGithub


text:
Seeing tiny arms and legs floating in the glass baking pan only makes woman "more pro-choice than ever"
You wouldn't expect moral (or any other kind of) clarity from someone who came to believe that working in an abortion clinic is the ultimate female bonding experience. And Amy Beeman proves to be no exception to the rule.
The title of her Salon piece–"Working at an abortion clinic challenged my pro-choice views — and confirmed them"– is typical of the genre. All those tiny arms and legs "floating in the glass baking pan" may have initially been a trifle startling, although nothing indicates Beeman experienced the normal human response: being grossed out.
She matter of factly observes, "The sucking noises during the aspiration sounded much like when they suck your spit at the dentist." The furthest she can go is, "It was all so heavy."
But as time went by Beeman came to see that these "recognizable" appendages were just so much "P.O.C." (Products of Conception). Under the tutelage of her "trainer," she concluded that a place of pain and misery and a "be quiet" abortionist (aka "our gentle and warm doctor ")
taught me that grace can be found in the unlikeliest of places. I saw the deep thread that binds us.
Not to the children, mind you, but to one another, aborting women and "counselors."
The operative paragraph is near the end. It both dismisses anyone silly enough to object and celebrates women's autonomy.
Abortion is fraught with so much negative sentiment, and in a sense, abortion is plucking a life from existence that has yet to have the opportunity to thrive. No one is claiming it's pleasant. There is nothing black and white about abortion. It's every shade of gray. But for us pro-choicers, the woman's life trumps the embryo or fetus. That's the bottom line. We place value on a woman's ability to know what is best for her. It simply needs to be a safe and legal option. When I started at the clinic, all I saw was the individual moment of pain, but over the years I began to see a much richer portrait — women coming out the other side, relieved, even if it was simply a relief to have the procedure behind them. They could go back to their lives.
So, nothing about abortion is "black and white"… except what is black and white: that "the woman's life trumps the embryo or fetus. That's the bottom line. We place value on a woman's ability to know what is best for her."
Pain? Sure, but what's a little "crying out in pain" compared to coming out the other side minus the P.O.C. and ready to "go back to their lives."
For Beeman, initially working in an abortion clinic beat being an unemployed recently graduated college student. In one of the many unintentionally telling remarks, she adds "It was a grown up job." To the people who ran this mom and pop abortion clinic it made no difference that she had no medical experience; what she didn't know they'd teach her on the job.
So what did Beeman have to offer? "I'd worked in hospitality as a waitress, and she said my people skills would be one of the most important things I'd bring to the job.
"Besides, I agreed with the mission of the organization. I was a feminist."
But she really hit her stride when she moved up from operating room lackey to counselor. From holding hands to explaining the "procedure" and philosophizing about the meaning of it all.
The perfunctory question she rhetorically asks along the way is, "Seeing every side, the whole complicated and profound process," would her feminist, pro-abortion views be altered? Not a chance: "I came out more pro-choice than ever."
In fact, the same woman who wrote of "the loneliness of those little arms and legs" and "That girl, so clearly suffering during the procedure" would "start to see it from a purely biological standpoint. We were removing an unwanted growth to preserve the woman's chosen course."
"Arms and legs" become "products of conception" become "unwanted growth."
The moral trajectory of Amy Beeman is complete.
Tags icon abortion
On-Line Pharmacy Selling Strawberry, Chocolate, and Marijuana Flavored Abortion Pills
National Right to Life Endorses Trent Kelly in Mississippi Special Runoff


text:
Posts Tagged 'forgiveness'
Zachary Levi: Mental Health, "Radical Love" & Forgiveness
By Kelli Gillespie | June 27, 2022
Whether you know him from Chuck or as Flynn Rider in Tangled, Joseph in The Star, or Shazam, Zachary Levi has risen to stardom and you would think it was a smooth ride. The now, 41-year-old shares that ir was far from it in his powerful new book Radical Love: Learning to Accept Yourself and…
MASS: Jason Isaacs & Fran Kranz
By Kelli Gillespie | October 7, 2021
Interviewed for Risen Magazine Risen Magazine: Fran, let's start with you. You wrote it, you directed it. I understand the idea. Talk me kind of how informed in the fact of your kind of history with school shootings and then becoming a parent and the thesis project you wrote, it was kind of the perfect…
MASS: Ann Dowd & Reed Birney
We hear too often of school shootings in America. Some are more visible than others but all have devastating, rippling effects. The new movie MASS, explores the aftermath of a violent tragedy. It's the meeting of two couples after the loss of their children… one the victim, the other the shooter. We have a deep…


When you want to get the most out of your FertilAid experience, reading reviews is a very important thing to do. This is because these reviews can help you to determine whether or not you may have a negative reaction to this treatment and can also help you find out if it may be the ideal way for you to increase your fertility. Here we will look at some of the reasons why looking at FertilAid Reviews is a great thing to do!
These reviews can help you to make better choices because they can help you keep your eye on the things that may mean that FertilAid is not the best choice for you. For instance, you can find reviews that talk about what happens when people who have certain health conditions take these supplements. If you have the same health conditions you can learn about how the treatment may react negatively or positively with these health conditions. It is important to know such things because you do not want to damage your body.
Another reason that reading FertilAid Reviews is an important thing to do is because the reviews can alert you to any negative reactions people experienced while taking other medications. Some herbs will prevent you from absorbing other medicines which you want to be aware of ahead of time. People who may be taking the same medications as you may share their experience through these reviews which will help you avoid making the same mistake.
Your doctor is a more important source of information than any FertilAid reviews. You always want to check with your doctor because they can help you be aware of any negative things that might happen from the combination of this supplement and your current medications or health conditions. When you talk to the doctor you can also share what you have learned from the reviews so that they are aware of what information you have gathered.
When you want to conceive a child, it becomes the most important thing. This is no reason to slack off on doing your research! You still want to read FertilAid Reviews so that you can be as aware as possible about what may happen if you choose to take this treatment.
It is also important to read as many FertilAid Reviews as possible so that you can see if anyone in your situation had a negative response. For instance if you have diabetes and have read that several people with this condition have had bad reactions to FertilAid, you can use that information and either find a new supplement or see if you can prevent the negative reactions somehow.


Powha collarless vest has a tailored silhouette that flows with the contours of the body and a front button closure. The vest has been hand cut and sewn from a heavy textured wool with a tonal cotton lining. Flat bonded seams are complimented by angular cuts to the neckline and two front pockets.


Prince Alwaleed bin Talal of Saudi Arabia looks like a big Twitter winner. So do the moneyed clients of Jamie Dimon.
But as you've-got-to-be-joking wealth washed over Twitter on Thursday — a company that didn't exist eight years ago was worth $31.7 billion after its first day on the stock market — the non-boldface name of the moment is Suhail R. Rizvi.
Twitter's successful opening also made gushing winners out of the usual suspects, including its co-founders Jack Dorsey and Evan Williams and a spate of more traditional tech investors. But Mr. Rizvi's rise illustrates how a new tech investor class with deep Wall Street connections is carving new paths into the unfamiliar and insular terrain of Silicon Valley.
Traditionally, stalwart venture capital firms like Kleiner Perkins Caufield & Byers and Accel Partners use their location in the Valley and their sizable funds to buy a piece of a start-up and have a hand in the company's direction. And Benchmark Capital and Spark Capital, two such companies, did get sizable pieces of Twitter.
But Mr. Rizvi and others like him have formed a network of investors, including prominent mutual fund companies like Fidelity, T. Rowe Price and BlackRock, that sometimes have even deeper pockets and a willingness to buy smaller pieces and assume no management control.
Many start-ups say they value the new source of hands-off capital from these investors outside Silicon Valley's palace intrigue. Aaref Hilaly, a venture capitalist at Sequoia Capital, a top venture company in the Valley, said the new type of investors could offer entrepreneurs some breathing room.
Still, some of Mr. Rizvi's counterparts on Sand Hill Road, the epicenter of venture capital, say the influx of new money will drive up start-up valuations in what is already a crowded part of the market.
Investors taking part in this new strategy include Chase Coleman, of the Manhattan hedge fund Tiger Global, who rolled a large pre-offering stake in Facebook into other hot start-ups, and Thomas Laffont, at Coatue Management, another Wall Street hedge fund that recently set up shop on Sand Hill Road.
But Twitter's public offering represents a coming-out party for Mr. Rizvi, who remains largely a mystery in Silicon Valley despite his growing number of tech investments. In Silicon Valley, and even at Twitter's San Francisco headquarters, prominent investors and executives said they had not heard his name until it popped up in public disclosures by the company last month.
Mr. Rizvi declined several requests for an interview. But interviews with nine people who are close to Mr. Rizvi or have worked with him on past deals, all of whom would speak only if they were not named, painted a picture of him as a strategic power broker who prefers to operate behind the scenes.
His money and connections have also led to slices of several other hot start-ups: Flipboard, the social news app; Pinterest, an online pinboard; Square, Jack Dorsey's payments service; and of course Twitter, Mr. Dorsey's other company.
Mr. Rizvi was born in India and raised in Iowa Falls, Iowa, and although he is known as a quiet man, he has built a powerful and diverse group of friends from his perch in Greenwich, Conn. Those in his circle include Richard Branson, founder of the Virgin Group, and Salar Kamangar, chief executive of YouTube. And Mr. Rizvi has been seen partying atop Eric Schmidt's yacht, the Oasis, at Cannes.
Before finding a path to Silicon Valley, Mr. Rizvi invested in telecoms and manufacturing, but his biggest moves were in the entertainment industry. In 2004, he asked a mutual friend to introduce him to Jeff Berg, then chairman of International Creative Management, the talent agency, which was looking to expand. A year later, Mr. Rizvi led a private equity investment in I.C.M., taking a controlling stake for $100 million — with $95 million in debt financing from Merrill Lynch.


This little beast is a very, very eerie and dark collection of metallic percussive and semimelodic effects, ideal for use in horror and suspend scoring, sound design, trailer effects and post production, experimental and ambient music, or anything else you can think to do with it. The origins of the strange creation/discovery will remain a forbidden secret – even though we had a contest where over 130 people tried to guess its origins. What we can tell you is that we beat and played the hell out of it… or in to it, depending on how you look at it. We used bows, mallets, metal rods, rubber balls, fingers, fists, drum sticks, nylon, metal brushes, rasps and files water and earth to lift all manner of demon song from this contraption.
We used viola bows on various edges of the throne to create eerie melodic and dissonant tones. There are several types of bowed instrument sets included, including long sustains, medium length gliss effects, short staccato and spiccato notes and tremelo sustain. There are a number of different kinds of mallet rub, brush, finger and comb effects to explore. The finger drabs, rubber ball and flat mallet rubs were created by dragging the edge of the mallets (or fingers) along various surfaces on the throne, in long arcs and lines. This causes it to resonate in a similar fashion to a gong when rubbed in the same manner. The mallet strikes were produced with wooden, felt and metal tipped mallets and sticks, to produce standard metal percussion sets. Generally, the results are atonal, but with pitch stepping/shifting, these instruments can be played semi-melodically.
This library is designed for the full retail version of Native Instruments Kontakt 3.5 or later. Kontakt is an industry-standard advanced virtual instrument software platform. You can view screenshots of this library's custom graphical user interface in the image gallery above. This library is packed with features to provide you a wide range of sound shaping parameter controls, each one totally automation-ready in your host environment or Kontakt's stand-alone mode. Learn more about Kontakt by Clicking Here.
The full retail version of Native Instruments Kontakt version 3.5 (or later) is required to use .nki instrument presets included in this library. The free Kontakt "Player" and "Add Library" import process do not support this standard open-format Kontakt library. Windows XP or higher. Mac OSX 10.6 or higher. Dual Core CPU, 2 GB System Ram, SATA or SSD hard drive recommended for this library.
"I especially enjoy the interesting and unique percussion instruments from SoundIron - everything from Iron Throne and Bamboo Sticks to AntiDrum and the Rust series."


The ECMM survey on histoplasmosis in Europe was set up in March 1997 to examine the epidemiology of this disease in Europe. It aimed to assess how frequently histoplasmosis occurred in Europe, where and how the infection was acquired, groups at risk, the causative organism, the methods by which the infection was diagnosed and what therapy patients received. The survey was carried out over a 5 year period, with information collected retrospectively from January 1995 to December 1997 and prospectively from January 1998 to December 1999. Members of eight countries participated in the survey, ECMM convenors were Prof. E. Glyn V. Evans and Ruth Ashbee (Mycology Reference Centre, Leeds, UK).
Overall, 127 cases were reported, with 70 occurring in the retrospective phase of the survey and 57 during the prospective phase. In the majority of cases, the patient had travelled to a known endemic area, although in a few cases there was no history of travel from their country of birth. The time that had elapsed between travel to endemic areas and presentation ranged from a few weeks to many years. Several of the patients from the UK had served in endemic areas during World War II and developed disseminated histoplasmosis after a delay of several decades, illustrating the importance of obtaining a lifelong travel history if histoplasmosis is sus pected. Commonly reported risk factors included exposure to birds, bats and caves and many patients had AIDS.
Methods of diagnosis varied between countries, with some using imaging techniques, whilst others favoured serological diagnosis. Treatment also varied between countries, ranging from none in those patients who had mild or asymptomatic disease, to long courses of systemic antifungals.
The number of cases reported from each country varied significantly and this is thought to be due to the ways in which the disease was diagnosed. In many instances, the diagnosis of histoplasmosis was incidental to other investigations. Because primary pulmonary histoplasmosis is often self-limiting, most cases will go undiagnosed and so the cases reported in the survey are likely to represent the 'tip of the iceberg'. Interim data were presented at the ECMM meeting in Barcelona in 2000 as an oral presentation, and several national convenors have presented the data for their country as posters at the ECMM and other meetings. The overall data collected during the survey are currently being prepared for publication.
The results were reported in: Ashbee HR, Evans EGV, Viviani MA, Dupont B, Chryssanthou E, Surmont I, Tomsikova A, Vachkov P, Ener B, Zala J, Tintelnot K and the ECMM Working Group on Histoplasmosis. Histoplasmosis in Europe: Report on an Epidemiological Survey from the European Confederation of Medical Mycology. Medical Mycology 2008; 46(1): 57-65.


At LiveDumpster, we strive to bring you the best service at the best price available. We have a wide selection of dumpster sizes in stock in Little Falls, NY, with flexible & timley pickup and delivery. Give us a call today for all your dumpster rental and roll off needs.
You can steer clear of the additional worth of having to subcontract a specialty cleaning company by using a dumpster located directly on the office space from the beginning of the project. Construction Cost of a Dumpster in Little Falls, NYs is usually delivered morning and can be purchased by phone or higher the internet. Your small business remodeling will look great and also run more smoothly when a dumpster is located on the webpage before the start of construction. After most tasks the site is rather messy, brimming with debris, using extra materials and soil needing to be picked up but you can stay clear of any potential extra washing by having a dumpster set up on spot right from the start.
Dumpsters come in different sizes and picking the right one is significant because the greater dumpster you choose, the more it will cost. For anyone who is doing a home renovating as well as home growth project, then you'll definitely have to cope with a lot of waste materials, which will demand a dumpster size of 30-40 cubic ft. Choosing the right dumpster dimensions not only determines the cost of these types of services, but it also determines how properly you can shift the waste materials from your home.
Junk comes with a way of accumulating quickly, even for the neatest of householders. That's what makes today's dumpster leases such an reasonably priced and vital service. Rather than having to land your vehicle and also spring intended for gas to make ten different trash excursions all the way locally, a Cost of a Dumpster in Little Falls, NY suitable at your home allows you to clear the whole lot a single fell pounce. From old, broken-down appliances and also discarded pieces of furniture to outdated electronics and remaining building materials, a roll-off Cost of a Dumpster in Little Falls, NY is only the ticket intended for disposing of the garbage from all of your family cleanup tasks inside and out, which include: Internet is the reservoir of information. All good and also reputable publication rack present on the net. You can use the internet right away and choose the company of your liking after a great research and also quote comparability. You can pay for the support online along with your credit card as well as through other options that the firm may give you.
No matter what project you are planning to accept, imagine all of the materials that you're going to be obtaining of the house. Visualize old home equipment, old cabinetry, maybe even the previous flooring. In every one of these scenarios, you have to want to do something with the things that you are getting rid of.
According to sanctioned reports of state of air, a lot of Americans are still suffering from the environment pollution which is being a reason for spreading several dangerous diseases like lung function, respiratory illness, lung redness and stress of breathing illness. Many of the residents with US live in detrimental and unhygienic environments. How can this be? The reason is very simple. The garbage generated due to several projects like construction, home renovation, business oriented clean ups and garden clean ups is left without right disposal and is contaminating the planet.
The Fifteen cubic yard Cost of a Dumpster in Little Falls, NY you will need if you are remodeling and collecting all of the particles from your home, a big car port, basement as well as attic clean-out for example if you are sorting through a home from the deceased individual, a large garden that needs massive clean up, or perhaps a basement remodeling job.
You will need a dumpster in order to throw out your paneling and aged carpeting within. You will need to toss the old entrance doors as well should you be replacing these individuals.
Remodeling your enterprise is an exciting occasion filled with targets and exhilaration. The business can be closed in the remodeling process or it usually is open with regard to business; in any event the business should look its best throughout the overall construction process. Construction web sites involve a substantial amount materials that are required for the upgrading process and also debris by materials being removed from your walls, your floors, and many others. There is no cause to expect the positioning to be a blunder. A structure Cost of a Dumpster in Little Falls can help the process significantly by that contain the blunder and particles in one closed down and safe and sound location.
The opposite question you are supposed to consider is whether it is possible to get ample space for your amount of junk. Firstly, you should realize that the quantity of space that you're going to get in just about any dumpster is going to be intensely dependent on the quantity of trash you've got. Usually, the quantity of space is often calculated within yards. Attempt to find out whether the respective corporation has the capacity to manage your size of trash. For just a garage full of junk, the 10 to 15 lawn dumpster is going to be suitable.
Trash taxi cab will know exactly the size of the vehicle that is needed for the job, and how practical the price can be. The possible companies that can be offered at an apartment difficult is substantial, and they will ensure that everyone's life is so much much better. Knowing that your own trash problems are dealt with is obviously a huge pain relief for any house block.
Dumpster rental fees offer individuals with dumping answers that are connected with very cheap and also affordable prices. These are simple, of top quality and trustworthy. Renting of dumpsters is incredibly easy as you're bound to select one in the nearest town. Because of the big competitors among the rental companies, you won't fail to find one that wont strain your financial allowance. They have a basic way of retaining in touch with buyers hence significantly less hassles and also time employed. These rental fees have direct phone traces that enable you to contact them as soon as you may need their services. Which you find in them trustworthy as you will always get the companies that you requested. The status for these companies continues to grow among the inhabitants because they supply the best of companies. You can search to the nearest Cost of a Dumpster in Little Falls locally from the service or the net.
Search for a Cost of a Dumpster in Little Falls corporation that is community and operates in your specific area. It's a good idea to use the help of your yellow pages or even the internet. Using a local support means that they shall be more likely to provide and recognise time as they are close by. A firm that is a nationwide chain may have unexpected setbacks which can provide unwanted and ugly trash in your yard longer than you desire. A local one can get to you more rapidly and has the chance provide better customer care.
Using a dumpster saves people an enormous amount of time. Garbage may be thrown into the bin, as opposed to piled up and taken to the get rid of. There is no rapid trip to the actual dump. Through the elimination of the need to get there, folks save time. On this occasion can be used to make money, rather than wait.
For people who tend to be anticipating undertaking a major development or demolition undertaking, or even a undertaking that entails both of these functions, it is quite feasible, highly most likely in fact, that the majority of debris is going to be generated.
Eliminating bathroom fittings and drywall can be a dirty and wearisome project. A do-it-yourself project can certainly leave a massive mess that won't have been regarded. Regular junk pickup from the community will be needing the extracting of the drywall and placing it in to bags. It'll likewise require how the bathroom fittings be got rid of off according to the township's rules. This can take time and require that your previous, stained bath sit away from your home whilst it waits for trash pickup. This is unwanted when you require a Cost of a Dumpster in Little Falls, NY. The size of the actual dumpster will be dependant on the size of the actual project and also the cost is dependant on the amount of junk disposed of. A Cost of a Dumpster in Little Falls, NY is affordable and supplies a quick signifies to remove unwanted waste within a bathroom renovation.
Precisely what is a dumpster? A huge metal container of diverse sizes that you can throw factors into. Nearly all project managers hire a dumpster or maybe two first, then get over it to the rest of the program. Homeowners who desires to renovate both room with their homes (especially the kitchen for instance), or the complete house, want to organize a new dumpster of the suitable size to complete the job as quickly and also as inexpensively as they possibly. It is a requirement, just like drywall and home appliances and flooring are. Minus something to placed the trash and recycle supplies in, after that where have you been going to input it? Dumpster rentals will benefit you inside above explained ways as long as you get associated with the ideal supplier who does definitely not give you negligence promise and run the appropriate to fetch your hard earned dollars. The service agency will be very fast in their products and services, fast pickup and fast delivery. The right Cost of a Dumpster in Little Falls, NY service provider is going to be transparent in it's rates to give the buyer prior info on the cost containing to be bared ahead of the order lies.
Renting a new dumpster makes it easy to take off the vast amount of yard dust left by the storm. Although most business oriented trash removal companies currently have strict guidelines about grass debris, Cost of a Dumpster in Little Falls, NY corporations do not have this kind of restrictions and can take whatsoever large braches or even woods trunks your neighborhood demands removed. With several neighborhood friends chipping in on the charge, it is also less expensive paying the fees that business oriented trash removal companies charge to remove increased yard detritus.
Everyone wants to keep their property and area clean and tidy. Huge amount of trash is definitely generated in house renovation and construction projects. These waste products cause difficulty at your employment location. To take off the clutter shaped at your employment location, you've got to find the great Cost of a Dumpster in Little Falls, NY service company.


Kanchan conducted a facilitative session on 'Advanced interdepartmental communication' with the top management of Suasth healthcare, Navi Mumbai. Thank you, Mr Neeraj Kumar and Mr Kalpit Mhatre for making me a part of this initiative for starting the process of creating a culture of open ' Dialogue' at Suasthhealthcare, This will enhance productivity and team performance. The core team has resolved to work towards being the best in the Industry of providing quality healthcare.
← Why and how to include Sarees in your workwear!


text:
Conhecido no Brasil como Baiacu de recife ou Cantigaster nacional (Canthigaster figueiredoi) é uma pequena espécie de baiacu do gênero canthigaster.

Distribuição
É endêmico do Brasil, podendo ser encontrado dês da Região Nordeste até Santa Catarina, incluindo o Atol das Rocas. Dizem que há avistamentos na Colômbia e Venezuela, mas na verdade esses avistamentos são do Canthigaster rostrata uma outra espécie de baiacu que se parece muito com ele.

Habitat
Habitam recifes rochosos e recifes de corais costeiros.

Alimentação
Se alimentam de algas, crustáceos, moluscos e esponjas.

Curiosidades
Seu nome científico figueiredoi faz homenagem a José Lima de Figueiredo, por suas contribuições para o avanço da taxonomia dos peixes marinhos brasileiros.É uma espécie inofensiva para os humanos, mas se for consumido pode acabar sendo envenenado pela sua tetrodotoxina (uma toxina comum de se encontrar em algumas espécies marinhas).

Em cativeiro
É muito popular pelos aquaristas brasileiros, pois é um peixe de fácil manutenção.

Tetraodontiformes
Tetraodontidae
Fauna endêmica do Brasil
Peixes do Brasil
Peixes descritos em 2002

redpajama_set_name: RedPajamaWikipedia


text:
void makeVerticallySymmetric(std::shared_ptr<figures::Curve> curve);
void makeHorizontallySymmetric(std::shared_ptr<figures::Curve> curve);
void makeTopBottomTree(Model &model, figures::PBoundedFigure root);

#endif // MODEL_OPS


text:
\section{Introduction}
\label{sec:intro}
Human performance capture plays a critical role in various computer graphics and vision applications such as virtual try-on, movies as well as video games.
With rapid progress in display and capture technology, expectations on the quality of geometric reconstruction and tracking are constantly increasing.
Here, not only the geometric details are of major importance but also that the deformed and posed reconstructions follow the physical behavior of real objects which includes realistic wrinkle patterns as well as coherent interaction of body and clothing.
While professional content production studios can rely on involved multi-camera setups to capture high-fidelity human performances, there is an ever-growing desire to democratize performance capture for everyday applications, \eg virtual try-on, by utilizing much simpler and cheaper capture devices.
\par
Hence, research has shifted from expensive and complex multi-view capture setups~\cite{pons15,bray06,cagniart10,brox10,pons17,de08,gall09,liu11,mustafa15,vlasic09, wu13,pons17} to depth cameras~\cite{slavcheva17,guo17,newcombe15, innmann2016,guo18, kowdle18,ye12, dou16, yu18, ye14,wei12,yu2019simulcap} over the past decade.
Unfortunately, the latter are sensitive to bright sunlight and thus are not suited for outdoor use-cases.
In conjunction with the advances in deep learning, the most recent research has shifted its attention onto \textit{single RGB camera} setups, offering the most flexible and low-cost setup.
Previous monocular methods have made a substantial progress in recovering the 3D unclothed body~\cite{hmrKanazawa17,pavlakos2018learning,kanazawa2019learning}, hand pose~\cite{wang_SIGAsia2020,GANeratedHands_CVPR2018,zhou2019monocular}, facial identity and expression~\cite{kim2018deep,tewari2017self,tewari17MoFA} as well as jointly tracking all of those~\cite{pavlakos2019expressive,xiang2019monocular,joo2018total, zhou2020monocular}.
However, only a few methods~\cite{habermann2020deepcap,habermann2019livecap,xu2018monoperfcap} coherently track the dense surface deformations with clothing included from monocular views, which is essential for a majority of applications.
These person-specific methods densely deform and pose a geometry to match the body pose and the clothing deformation in the input image while assuming an initial template of the person is given.
Recent learning-based monocular methods~\cite{habermann2020deepcap} only leverage image-based supervision, rendering it a challenging task to densely supervise deformations.
This manifests in simplified model assumptions, \eg, a single geometry for both body and clothing, or the utilization of simple geometric priors disobeying physics principles.
Consequently, they either fail to account for body-cloth interactions or having deformations that do not follow physical rules. Importantly, artifacts such as baked-in wrinkles from the initial scan are highly noticeable in their results.
\par
To this end, we propose a learning-based approach for capturing the body pose and the physically plausible clothing deformation from a single RGB image (see Fig.~\ref{fig:teaser}).
Our method comprises two networks dedicated to regress body pose in terms of joint angles and surface deformations in form of embedded deformation.
Importantly, during training, we only assume weak supervision with multi-view imagery, \ie 2D skeletal joint detection, and foreground masks.
These supervisions alone can hardly ensure physically plausible results.
Thus, at the core of our approach, we propose an efficient simulation layer that for the first time allows physically plausible self-supervision \textit{during} the training in such a weakly supervised setting.
We achieve this by integrating a physics-based simulator into a learning architecture that takes intermediate predictions of cloth and body positions and velocities to perform forward simulations.
The simulation results are then used to supervise the cloth deformations during training.
As cloth-body collisions are explicitly handled in the proposed layer, we can accurately model clothing as a separate piece of geometry in contrast to previous monocular methods.
In summary, our contributions are:
\begin{itemize}
\item A monocular human performance capture approach, which outputs body pose and physically plausible cloth deformations for dressed subjects.
\item A simulation network layer that allows on-the-fly simulation supervision \textit{during} training, which also enables separate modeling of cloth and body geometry.
\end{itemize}
In contrast to prior work, our method reconstructs physically more accurate deformations without baked-in wrinkles and with correct body-cloth collision handling.
Our quantitative evaluations indicate that incorporating physics-based simulation during training provides significant improvements over state-of-the-art methods.

\section{Related Work}
\label{sec:relatedwork}
As our goal is recovering a dense surface of the human, we focus on previous works that achieve this by using parametric body models or template meshes, and works that treat body and clothing as separate mesh layers.
We omit the works on 2D~\cite{cao18,cao17,simon17,wei16} and 3D skeletal pose estimation~\cite{VNect_SIGGRAPH2017,XNect_SIGGRAPH2020,inthewild3d_2019,rhodin2018learning,popa2017deep,zhou2017towards,sun2017compositional,tome2017lifting,rogez_lcr_cvpr17} as they are not concerned with the problem of surface reconstruction.
\paragraph{Reconstruction of Parametric Body Models.}
The works~\cite{zhou2010parametric,jain2010movie,rogge2014garment,guan2009estimating, bogo2016smpl,Lassner,hmrKanazawa17,varol18_bodynet} that fall into this category use parametric body models~\cite{loper2015smpl}.
Some works fit the model parameters to sparse 2D and 3D joint predictions~\cite{bogo2016smpl} or regressed vertex positions~\cite{kolotouros2019convolutional} by minimizing corresponding energies.
Others~\cite{hmrKanazawa17} directly regress these parameters from images.
A set of recent works~\cite{pavlakos2019expressive,xiang2019monocular} extended body models to account for varying hand poses and facial expressions to jointly capture hands, face, and body.
While motion and shape of the undressed body are reconstructed, clothing is not considered.
\paragraph{Unified Reconstruction.}
One stream of previous work treats body and clothing as a single geometry.
Volumetric representations~\cite{zheng2019deephuman,varol2018bodynet} use an occupancy grid to represent the body, meaning that the resolution is limited by the grid.
Implicit methods~\cite{saito2019pifu,huang2020arch,saito2020pifuhd} methods overcome this limitation by treating the surface as an implicit function.
However, both approaches require post-processing to recover explicit surface representations.
Lacking temporal consistency thus prohibits these approaches for applications such as texture replacement or motion retargeting.
Closely related to our work are template-based methods~\cite{habermann2019livecap, habermann2020deepcap, xu2018monoperfcap,carranza2003free, de2008performance} that track a template based on image observations.
Using a mesh with fixed topology as a reference, surface correspondence over time is explicitly given.
With input data originating from images only, this ill-posed problem is countered by simplified assumptions and geometric priors.
Consequently, static wrinkles contained in a template remain visible across all poses, and deformations commonly appear to be physically implausible, \eg defying gravity.
Most importantly, all these methods treat clothing and body as a single piece of geometry ignoring dynamic body-cloth interactions.
To address these limitations, we propose a simulation layer that encourages cloth deformations to not only satisfy image constraints but also exhibit physically plausible behavior.
\paragraph{Cloth as a Separate Part.}
In contrast to the above methods, there is also a line of work that reconstructs body and clothing as separate geometries.
Bhatnagar \etal~\cite{bhatnagar2019mgn} recover static geometry for clothing and body from a set of RGB images.
DeepWrinkles~\cite{lahner2018deepwrinkles} enables posing a piece of cloth where their method learns to regress pose-dependent wrinkles at high resolution.
ClothCap~\cite{pons2017clothcap} uses multi-view capture to produce a clothed human body that can be used for re-targeting.
Stoll \etal~\cite{stoll2010video} recover cloth material parameters from multi-view video sequences to reproduce the observed garment deformation.
SimulCap~\cite{yu2019simulcap} performs quasi-static physics simulation with depth matching constraints to reconstruct the clothing layer.
Different from the above methods, our approach relies solely on a monocular RGB camera.
Also leveraging simulation, MulayCap~\cite{su2020mulaycap} recovers, both, the texture and the geometry of a dressed subject from monocular RGB videos by a multi-layer decomposition approach.
However, simulation is only used to generate initialization for the succeeding refinement stage, whereas we consistently enforce simulation supervision.
\par
As a potential alternative to simulation, geometric detail such as wrinkles can be added in a data-driven, pose-dependent manner \cite{guan2012drape,santesteban2019learning,patel2020tailornet,gundogdu2019garnet}.
Different from these geometry-driven methods, we integrate physics-based simulation into our training framework thus encouraging physical plausibility with only a single image as input.

\section{Method}
\label{sec:method}
Our template-based method leverages a deep neural architecture, taking a single background-segmented person image as input and regresses posed and deformed surface meshes for body and clothing which match the performance in the input image (Fig.~\ref{fig:pipeline}).
Before training, a 3D template of the person with separate cloth and body geometry and a multi-view recording of the subject performing various motions has to be acquired (Sec.~\ref{sec:method_data}).
The technical core of our architecture is formed by two prediction networks, \textit{PoseNet} and \textit{PADefNet}, that are trained to regress body pose and physics-aware cloth deformation, respectively (Sec.~\ref{sec:method_surface_prediction}).
\textit{PoseNet}~\cite{habermann2020deepcap} regresses skeleton joint angles and the root rotation from the input image using multi-view 2D joint detection as weak supervision.
The proposed \textit{PADefNet} predicts the surface deformation of the cloth template by regressing embedded graph parameters from the same input image.
In addition to multi-view image data, \textit{PADefNet} leverages our cloth simulation layer as supervision, which encourages physically plausible deformations (Sec.~\ref{sec:method_sim}).
\begin{figure*}[ht]
    %
    \centering
    %
    \includegraphics[width=\textwidth]{imgs/PipelineFinal.png}
    %
    \caption{
    %
    Our method takes a single image as input and two networks, \textit{PoseNet} and \textit{PADefNet}, regress the skeletal pose as well as embedded deformation parameters for the clothing.
    %
    Combining the outputs of the two networks allows posing and deforming the body and clothing geometry.
    %
    During training, we use multi-view image losses for \textit{PoseNet} and \textit{PADefNet} is additionally supervised by our proposed simulation loss to encourage physically plausible deformations.
    %
    To evaluate the simulation loss, we run on-the-fly cloth simulation on small windows of subsequent frames from the training sequence and penalize the difference between regressed deformations and simulation outputs.
    %
    }
    %
    \label{fig:pipeline}
    %
\end{figure*}
\subsection{Data Processing}
\label{sec:method_data}
\paragraph{Template Acquisition.}
\label{sec:tempalte_acquisition}
Similar to DeepCap\cite{habermann2020deepcap}, we acquire a single scan for body and clothing (\eg using photogrammetric scanning).
Surface registration against a parametric body mesh model
\cite{alldieck19cvpr,bhatnagar2020ipnet} is then performed to obtain an estimate for body parts occluded by clothing, \eg the legs under a skirt, which are merged with the visible body parts from the scan to form a complete body mesh.
The arms of the body mesh are labeled as inactive when resolving collisions.
A separate cloth mesh is created manually from the scan, a task could also be automated~\cite{stoll2010video,pons2017clothcap}.
Skeleton parameters and skinning weights, required for posing the meshes, are determined automatically~\cite{habermann2020deepcap}.
Two separate embedded graphs~\cite{sumner2007embedded,sorkine2007rigid} for body and clothing are computed by down-sampling the original meshes.
These pre-processing steps only need to be done once per character.
For more details, we refer to the supplemental document.
\paragraph{Video Capture.}
We capture the subject to be tracked in a multi-view green screen studio with calibrated and synchronized cameras.
The person is asked to perform various tasks, \eg walking, and dancing, to best sample the space of possible poses.
Next, we apply OpenPose~\cite{cao17,cao18,simon17,wei16} on all frames and views to obtain multi-view 2D joint predictions.
Color keying is used to segment the foreground from the green-screen background and compute distance transformation images $\boldsymbol{D_c}$ from the foreground masks~\cite{borgefors86}.
\subsection{Cloth Simulation Layer}
\label{sec:method_sim}
Our simulation layer uses the publicly available cloth simulation framework ARCSim~\cite{narain2012adaptive} as its basis, but we make several adjustments that we describe below.
\paragraph{Material Model and Parameter Selection.}
ARCSim leverages a data-driven material model defined by a total of 39 parameters.
While parameter values for several real-world fabrics are provided, we found that none of them were ideally suited for the materials that we use in our examples, and manually adjusting parameters to obtain better approximation proved very difficult.
For this reason, we resorted to a simpler, isotropic material model\cite{thomaszewski2008asynchronous} defined through three parameters:
Young's modulus and Poisson's ratio for in-plane behavior, and a single bending stiffness coefficient.
We determine parameter values through best-guess initialization and a few iterations of simulation-based tuning to better approximate the qualitative behavior observed in the input video sequences.
We used the same parameters for all sequences.
Although this manual approach is sufficient for our examples, this task could be further automated~\cite{stoll2010video}.
\paragraph{Time Integration.}
During training, the initial state and velocities that are fed into the simulation layer can exhibit large deformations that, when using ARCSim's default integration method, can lead to instabilities.
To improve stability, we resort to an optimization-based formulation of fully implicit Euler~\cite{Martin10EBEM} combined with adaptive regularization and a back-tracking line search.
Finally, we make several code adaptations to enable batch operations for efficient training and integrate the simulation engine in a customized TensorFlow\footnote{https://www.tensorflow.org/} layer.
We refer to this layer as the simulation function $\mathcal{S}$, which takes cloth and body vertices as input and returns the cloth positions for the next time step.
\paragraph{Silhouette Constraint.}
While our simulation model captures the characteristic behavior of clothing, it is still an approximation, and deviations from the input images must be expected due to external forces such as air drag, viscous damping, and friction that are not modeled.
To better track the real-world behavior, we add a multi-view silhouette constraint term.
Specifically, this constraint ensures that the vertices $\boldsymbol{\Tilde{V}}_{\mathrm{cloth}}^t$ of the simulated cloth geometry matches the image silhouettes from all camera views for frame $t$.
We construct a 3D ray going through the camera origin and the silhouette pixel $p$ and search for the boundary vertex $\boldsymbol{\Tilde{V}}_{\mathrm{cloth},p}^t$ that minimizes the distance to this ray.
The closest point on the ray is used as 3D point correspondence for the boundary vertex, enforced via soft constraints
\begin{equation}
    E_{cons} =\sum_p ||\boldsymbol{\Tilde{V}}_{\mathrm{cloth},p}^t - \boldsymbol{V}_{\mathrm{ray},p}^t||^2 .
\end{equation}
\subsection{Pose and Deformation Regression}
\label{sec:method_surface_prediction}
We separate the task of regressing the full surface deformation into predicting pose and surface deformation independently.
Therefore, our method consists of two ResNet50 based CNNs~\cite{he2016deep}, \textit{PoseNet} and \textit{PADefNet}, which regress skeleton pose and embedded deformation parameters from a segmented input image, respectively.
\subsubsection{Pose Regression and Deformation Model}
To pose and deform template vertices as well as sparse body markers, a deformation layer~\cite{habermann2020deepcap} denoted as
\begin{equation}
\label{eq:posedeformationmodel}
    \boldsymbol{V}_{loc},\boldsymbol{K}_{loc} = f(\boldsymbol{\theta}, \boldsymbol{\alpha}, \boldsymbol{A}, \boldsymbol{T})
\end{equation}
is used, which is a combination of dual quaternion skinning~\cite{kavan07} and embedded deformation~\cite{sumner2007embedded,sorkine2007rigid}.
It takes the pose in terms of skeleton joint angles $\boldsymbol{\theta} \in \mathbb{R}^{3}$ and camera-relative root joint rotation $\boldsymbol{\alpha}\in \mathbb{R}^{3}$ as well as the embedded graph node rotation $\boldsymbol{A} \in \mathbb{R}^{K \times 3}$ and translation $\boldsymbol{T} \in \mathbb{R}^{K \times 3}$ where each row encodes rotations in terms of Euler angles and translation vectors for each of the $K$ nodes.
The output is the posed and deformed vertices $\boldsymbol{V}_{loc}$ and markers $\boldsymbol{K}_{loc}$ in camera and root-relative space
.
The body pose parameters $\boldsymbol{\theta}$, $\boldsymbol{T}$ and $\boldsymbol{\alpha}$ are obtained from \textit{PoseNet}~\cite{habermann2020deepcap}.
\subsubsection{Physics-aware Deformation Regression}
To not only pose the template but also account for surface deformation, a dedicated network \textit{PADefNet} predicts the translation vectors $\mathbf{T}$ and rotation angles $\mathbf{A}$ of the embedded graph (EG) from the segmented input image.
\textit{PADefNet} is supervised using a combination of both image-based and physics-based metrics, which ensure that deformations match image-based observations while minimizing violations of physical equilibrium conditions.
In the remainder of this chapter, we assume \textit{PoseNet} is fixed and provides the posed and deformed vertices $\boldsymbol{V}$ and markers $\boldsymbol{K}$ in global space.
As $\boldsymbol{V}$ and $\boldsymbol{K}$ are a function of the \textit{PADefNet} outputs ($\mathbf{T}$, $\mathbf{A}$), we can then supervise \textit{PADefNet} on $\boldsymbol{V}$ and $\boldsymbol{K}$.
\paragraph{Warm Start.}
To jump-start our training including the simulation layer, we first pre-train \textit{PADefNet} without running simulation but use a geometric regularizer (ARAP~\cite{sorkine2007rigid}).
This adds robustness to the training as geometric regularizers are more stable than simulation and significantly reduces overall training time. Once the network predicts reasonable shapes, we add the simulation loss to supervise the physical deformation.
The loss is defined as
\begin{equation}
   L_{\mathrm{warm}} = L_{\mathrm{sil}} + L_{\mathrm{lm}} + L_{\mathrm{reg}} + L_{\mathrm{att}}
\end{equation}
which comprises multi-view losses as well as geometric priors. The individual loss terms are defined as follows.
\paragraph{Multi-view Losses.}
Our multi-view 2D landmark loss
\begin{equation}
\label{lm_loss}
    L_{\mathrm{lm}} = \beta_{\mathrm{lm}} \sum_c \sum_{m} ||\Pi_c(\boldsymbol{K}_{m}) - \boldsymbol{p}_{c,m}||^2
\end{equation}
ensures that the projected landmark matches the 2D detection $\boldsymbol{p}_{c,m}$ for all views $c$ and landmarks $m$.
Here, $\Pi_c$ denotes the projection function of the camera $c$.
To densely supervise the surface, we also introduce a silhouette loss
\begin{equation}
\label{sil_loss}
     L_{\mathrm{sil}} = \beta_{\mathrm{sil}} \sum_c \sum_{b\in \mathcal{B}_c} \rho_{c, b} ||\Pi_c(\boldsymbol{V}_b) - \boldsymbol{D_c}||^2,
\end{equation}
which ensures that the set of mesh boundary vertices $\mathcal{B}_c$ matches the zero contour line in the distance transformation image $\boldsymbol{D_c}$ for all views.
$\rho_{c, b}$ is a weighting term ensuring that silhouettes are only matched if the normal of the surface aligns with the gradient of the distance transformation~\cite{habermann2019livecap}.
\paragraph{Regularization Loss.}
To regularize deformations and to avoid drifting of the surface, we employ the as-rigid-as-possible prior~\cite{sorkine2007rigid} to ensure smooth local embedded deformations.
We further adopt the rigidity weights formulation~\cite{habermann2020deepcap} to model material-dependent deformation behaviors, \eg the skirt can deform more freely than the skin.
\paragraph{Attachment Loss.}
Note that our entire mesh $\boldsymbol{V}$ can be split into body and garment meshes, denoted as $\boldsymbol{V}_{\mathrm{cloth}}$ and $\boldsymbol{V}_{\mathrm{body}}$ in the remainder of this section.
To ensure a coherent movement of these two, an attachment loss
\begin{equation}
\label{att_loss}
    L_\mathrm{att} = \beta_\mathrm{att} \sum_{i \in \mathcal{A}}  ||\boldsymbol{V}_{\mathrm{cloth},i} - \sum_{j=0}^2 \gamma_{i,j} \mathcal{C}(\boldsymbol{V}_{\mathrm{cloth},i},\boldsymbol{V}_{\mathrm{body}})_j||^2
\end{equation}
is included to ensure that the cloth is attached to the body at some anchor positions, \eg the waistband of a skirt has to be attached to the hip of the body mesh.
Here, $\mathcal{A}$ are the selected vertices on the garment that act as anchor points, $\mathcal{C}$ is a function that takes the cloth vertex id $i$ and returns the 3 vertices of its closest triangle on the undeformed body mesh, and $\gamma_{ij}$ are barycentric weights computed from the closest point on this triangle and its three vertices.
\paragraph{Physics-aware Training.}
\label{sec:method_phys_network}
While the previous training stage constrains the surface mesh to match the image evidence, it can neither account for the collision of body and clothing nor ensure physically plausible cloth deformations.
To this end, we introduce a dedicated simulation-based loss as a better substitution for the ARAP term to explicitly penalize collision behavior and physically implausible deformations.
Our final loss is then defined as
\begin{equation}
    L = L_{\mathrm{sil}} + L_{\mathrm{lm}}  + L_{\mathrm{sim}} + L_{\mathrm{att}}.
\end{equation}
\par
As our simulation layer $\mathcal{S}$ is directly integrated into a learning framework, we can perform on-the-fly simulation during training.
While our method takes a single image as input, the simulation-based loss term is designed to be a multi-frame function to better leverage the sequential training data available.
More concretely, this term penalizes the accumulated error on a set of consecutive frames, \ie the mismatching between the per-frame predictions and the on-the-fly simulation results within a frame window (see Eqn.~\ref{eqn:sim_loss}).
We found that in practice performing simulation over long sequences is extremely challenging when using shapes and poses predicted by a network, since even visually unnoticeable errors, \eg cloth getting trapped in body self-intersections, can lead to catastrophic failures.
Hence, we designed our framework specifically to rely only on small simulation windows $\mathcal{F}$ starting at random frames $t'$ to have shorter but successful simulations for training.
Additionally, the chosen design is well suited for machine learning, as it allows to access data randomly and in parallel for training.
In the following, we refer to a specific frame in this window using the superscript $\cdot^t$, where $t \in \{t', ..., t'+ \mathcal{F} \}$.
Our physics loss then reads
\begin{equation}
\label{eqn:sim_loss}
    L_\mathrm{sim} = \beta_\mathrm{sim} \sum_{i} \sum_{t=t'+1}^{t'+ \mathcal{F}} ||\boldsymbol{V}_{\mathrm{cloth},i}^{t} - \boldsymbol{\Tilde{V}}_{\mathrm{cloth},i}^{t}||^2.
\end{equation}
Here, $\boldsymbol{\Tilde{V}}_{\mathrm{cloth},i}^{t}$ denote the post-simulation cloth vertex positions, defined as
$$
\boldsymbol{\Tilde{V}}_{\mathrm{cloth}}^{t} =
   \begin{cases}
        \boldsymbol{V}_{\mathrm{cloth}}^{t}, & t = t'  \\
        \mathcal{S}( \boldsymbol{V}_{\mathrm{cloth}}^{t-1}, \boldsymbol{V}_{\mathrm{cloth}}^{t}, \boldsymbol{V}_{\mathrm{body}}^{t-1},  \boldsymbol{V}_{\mathrm{body}}^{t}),              & t = t'+1\\
        \mathcal{S}(\boldsymbol{\Tilde{V}}_{\mathrm{cloth}}^{t-2}, \boldsymbol{\Tilde{V}}_{\mathrm{cloth}}^{t-1}, \boldsymbol{V}_{\mathrm{body}}^{t-1},  \boldsymbol{V}_{\mathrm{body}}^{t}), & t > t'+1
    \end{cases}
$$
where $\mathcal{S}$ is the aforementioned simulation operation.
We initialize the cloth position with \textit{PADefNet} outputs at $t = t'$, where no history is available, and the velocity is initialized using finite difference with cloth position between the succeeding frame.
The body vertices positions come from the network predictions and velocities are computed always with finite differences.
The loss is then evaluated on the $\mathcal{F} - 1$ frames (excluding the first frame).
Even though the first frame in a training sample sequence does not receive this supervision, that frame is supervised by our multi-view supervision, such that in practice all frames are supervised.
We opted to not backpropagate gradients through the simulation inputs with respect to the EG parameters as guaranteeing convergence during training would be harder.

\section{Results}
\label{results}
We evaluate our approach on various outdoor and indoor environment settings with three subject-cloth combinations under a wide range of motions (see Fig.~\ref{fig:our_results}).
To bridge the domain gap between training data recorded in the capture studio and in-the-wild testing sequences, \eg different light conditions, we apply a domain adaptation step.
\textit{PoseNet} and \textit{PADefNet} are refined for 300 iterations on the testing sequence leveraging the losses introduced before but \textit{using only a single camera}.
For in-the-wild captures with varying and dynamic backgrounds, we segment the input images using OSVOS~\cite{caelles17}.
While the result is almost collision-free thanks to \textit{PADefNet}, minute intersections can remain, which is why we run a final collision resolution step (see also supplemental video).
This optional step takes 2s per frame on an Intel i7-9700 CPU.
Following DeepCap~\cite{habermann2020deepcap}, we apply a temporal Gaussian filter of size 5 frames.
\vspace{-10pt}
\paragraph{Dataset.}
Our training dataset contains 3 green screen studio capture sequences with actors performing a large range of motions.
For testing, we recorded an additional multi-view green screen sequence to evaluate our reconstruction on reference views and multiple in-the-wild captures using a single camera with a resolution of $1920 \times 1080$ for every subject.
Apart from a public available sequence \textit{S4}~\cite{habermann2020deepcap}, we additionally acquired two training sequences and templates, \textit{F1} and \textit{F2}, with 18 cameras at a resolution of $1285 \times 940$, where each sequence contains around 20,000 frames.
We will release the dataset for future research.
\begin{figure*}[t]
    \centering
    \includegraphics[width=\textwidth]{imgs/qualitative.jpg}
    \caption
    %
    {
    %
    Reconstructions obtained with our method for various in-the-wild environments and challenging motion combinations.
    %
    Our results show good overlay quality throughout, attesting to the pose and clothing estimation accuracy of our method. Furthermore, diverse and physically plausible cloth deformations are observed for a wider range of poses.
    %
    }
    \label{fig:our_results}
    %
\end{figure*}
\vspace{-10pt}
\paragraph{Qualitative Results.}
In Fig.~\ref{fig:our_results}, we test our method on various in-the-wild environments while the subjects perform a wide range of motions.
Our method does not only provide accurate image overlays and plausible 3D body and cloth geometries but our reconstruction also show physics-aware cloth deformations and plausible body-cloth interactions.
\textit{PADefNet} predicts different physically plausible wrinkle patterns related to the character motion as shown in Fig.~\ref{fig:dynamic_wrinkle}.
This is due to our separate modeling of body and cloth geometry and the fact that body-cloth interactions are taken into account by our simulation supervision.
We further visualize the underlying body geometry without clothing where also the occluded parts are predicted accurately.
\begin{figure}[t]
    \centering
    \includegraphics[width=\linewidth]{imgs/movement.png}
    \caption{
    %
    Reconstruction results.
    %
    Despite minor distortions in occluded regions, our reconstructed body geometry matches the image evidence.
    %
    Thanks to the separate modeling, our cloth reconstruction is able to reproduce the folding and unfolding behavior of the dress driven by the underlying body motions.
    }
    \vspace{-3mm}
    \label{fig:dynamic_wrinkle}
\end{figure}
\subsection{Comparisons}
We compare our approach to the state-of-the-art template-based monocular human performance capture methods~\cite{habermann2019livecap, habermann2020deepcap}.
LiveCap~\cite{habermann2019livecap} optimizes the pose via inverse kinematics to match predicted 2D and 3D joint positions and computes surface deformations via analysis by synthesis.
DeepCap~\cite{habermann2020deepcap} uses weak supervision from multi-view images during training to predict pose and embedded deformation parameters from a single segmented image.
\subsubsection{Qualitative Comparisons}
In Fig.~\ref{fig:comparison}, we compare our method with state-of-the-art template-based methods~\cite{habermann2019livecap,habermann2020deepcap}.
Unlike our approach, both LiveCap and DeepCap only use geometric priors on the deformations during optimization and training, respectively.
Consequently, the resulting cloth deformation contains static wrinkles from the initial template (see top left corners) that persist across all poses.
By using simulation supervision and separate modeling of cloth and body geometries, the wrinkles generated by our method are less constrained by the template and, consequently, exhibit more variety and better physical plausibility.
\begin{figure}[t]
    \begin{center}
    \includegraphics[width=0.11\textwidth]{imgs/comparison/input.png}
    \includegraphics[width=0.11\textwidth]{imgs/comparison/livecap_wt.png}
    \includegraphics[width=0.11\textwidth]{imgs/comparison/deepcap_wt.png}
    \includegraphics[width=0.11\textwidth]{imgs/comparison/ours_wt_blue.png}
	\bmethodlabel{0.01cm}{Input}
	\bmethodlabel{1cm}{LiveCap~\cite{habermann2019livecap}}
	\bmethodlabel{0.7cm}{DeepCap~\cite{habermann2020deepcap}}
	\bmethodlabel{0.8cm}{\textbf{Ours}}
    \end{center}
    \caption{
    %
    Comparison with state-of-the-art template-based monocular methods~\cite{habermann2019livecap,habermann2020deepcap}.
    %
    Using simulation supervision during training, our method produces physically more realistic results without baked-in wrinkles from the initial template mesh (shown in the top left corners).
    %
    }
    \label{fig:comparison}
    \vspace{-2mm}
\end{figure}
\subsubsection{Quantitative Comparisons}
We evaluate our results using the green screen testing sequence of \textit{S4} for all metrics below.
Note that obtaining accurate ground truth 3D geometry from such a sparse camera system is impossible and thus we resort to image-based and physics-based metrics.
For a fair comparison, we use the same cloth-body geometry, obtained through manual clean-up of the input scans, for all approaches.
\paragraph{Out-of-balance Force Evaluation.}
In Tab.~\ref{tab:oob_force}, we list the magnitude of the out-of-balance forces, which are defined as the difference between inertial forces and the sum over internal, external, and collision forces.
This physical measure indicates to what extent the results deviate from Newton's second law of motion and vanishes for physically correct motion.
The acceleration of the body and the garment for a given frame is determined using a centered difference approximation based on network predictions for three consecutive frames.
To reduce the global translation error irrespective of our network predictions, we apply the ground truth global translation for all methods as described by Habermann \etal~\cite{habermann2020deepcap}.
Our method performs not only better on average compared to other approaches but also significantly reduces the peak value.
LiveCap~\cite{habermann2019livecap} performs significantly worse due to the inherent ambiguity of the single-image setting combined with the inability of geometric priors to capture physical behavior. As a result, the cloth geometry returned by their method exhibits large distortions, in particular in regions occluded from view, resulting in large internal forces.
DeepCap~\cite{habermann2020deepcap} leverages neural network models trained with multi-view supervision.
Despite substantial improvements compared to LiveCap, our physics-aware method leads to a 50\% decrease in error.
\begin{table}[t]
\begin{center}
\begin{tabular}{ |p{2.5cm}|p{2.2cm}|p{2.2cm}|}
 \hline
 Methods     & Avg & Max\\
 \hline
 LiveCap\cite{habermann2019livecap} & 79.85 &1140\\
 \hline
 DeepCap\cite{habermann2020deepcap}   &2.119& 29.84\\
 \hline
 Ours   & \textbf{1.017} & \textbf{7.063} \\
 \hline
\end{tabular}
\end{center}
\caption{
Out-of-balance force evaluation.
We compare our method to LiveCap~\cite{habermann2019livecap} and DeepCap~\cite{habermann2020deepcap} with respect to out-of-balance force magnitude.
It can be seen that our physics-aware method outperforms state-of-the-art geometry-based methods.
}
\label{tab:oob_force}
\vspace{-4mm}
\end{table}
\paragraph{Garment-body Intersection Distance.}
In Tab.~\ref{tab:ixn_dis}, we further compare the garment-body intersection distance to LiveCap~\cite{habermann2019livecap} and DeepCap~\cite{habermann2020deepcap}.
Their simplified one-piece templates sidestep this issue, however, when we evaluate with our more accurate mesh model, \eg separate geometry for clothing and body, collisions severely affect the reconstruction quality.
We show that our network predictions significantly reduce cloth penetration.
\begin{table}[t]
\begin{center}
\begin{tabular}{ |p{4.5cm}|p{3cm}| }
 \hline
 \multicolumn{2}{|c|}{ Average Penetration Depth (cm)} \\
 \hline
 Methods     & Distance \\
 \hline
 LiveCap\cite{habermann2019livecap}  & 25.58 \\
 \hline
 DeepCap\cite{habermann2020deepcap}  & 23.83 \\
 \hline
 Ours   & \textbf{4.165} \\
 \hline
\end{tabular}
\end{center}
\caption{Penetration Depth.
    %
    We compute average penetration depths for cloth-body intersections across a 10,000 frame testing sequence. Not taking collisions into account, both LiveCap~\cite{habermann2019livecap} and DeepCap~\cite{habermann2020deepcap} produce severe penetrations.
    %
    Our method handles collisions during training, which leads to substantially reduced penetration depths.
    }
\label{tab:ixn_dis}
\end{table}

\paragraph{IoU Percentage.}
To measure reconstruction quality from different camera views, we compare our results with previous methods using the intersection over union (IoU) metric (see Tab.~\ref{tab:iou_loss}).
The IoU metric indicates the overlapping percentage of the camera projection images of our reconstruction and the foreground segmentation of input images (ground truth).
To be consistent with DeepCap~\cite{habermann2020deepcap}, the evaluation is performed for every 100th frame of the testing sequence from \textit{S4}, and we apply the same ground truth global translation and a temporal filter.
Comparing to body-only reconstruction methods, our method achieves significantly better performance.
It should be noted that, compared to the other approaches, the cloth geometry in our method is more constrained due to physics. For example, the strap of a dress cannot detach from the body to match the image silhouettes.
Nonetheless, we achieve comparable IoU accuracy while maintaining better physical plausibility.
\begin{table}[t]
\begin{center}
\begin{tabular}{|c|c|c|c|}
 \hline
 Methods     & AMVIoU (\%) & RVIoU(\%) & SVIoU(\%)\\
 \hline
 HMR\cite{hmrKanazawa17} & 65.10 & 64.66 & 70.84\\
 \hline
 LiveCap\cite{habermann2019livecap} & 59.96 &59.02 &72.16\\
 \hline
 DeepCap\cite{habermann2020deepcap}   & 82.53& 82.22 & 86.66\\
 \hline
 Ours   & 80.83 & 80.53 & 84.83\\
 \hline
\end{tabular}
\end{center}
\caption{IoU percentage comparison.
Average multi view (\textit{AMVIoU}), reference view (\textit{RVIoU}) and single view (\textit{SVIoU}) values correspond to IoU evaluation on all views, all views expect input view, and input view, respectively.
Our reconstruction provides comparable accuracy with state-of-the-art methods while delivering more physically plausible results.}
\label{tab:iou_loss}
\end{table}
\subsection{Ablation Study}
\paragraph{Simulation \textit{during} Training.}
Here, we verify that, with simulation supervision in the training process, physically unrealistic cloth deformations and other artifacts resulting from merely image-based supervision can be reduced.
In Fig.~\ref{fig:training_with_sim}, the strap of the dress remains on the body, and the bottom of the dress does not distort to match the silhouette.
\begin{figure}[ht]
    \centering
    \includegraphics[width=\linewidth]{imgs/ablation_all_h2.png}
    \caption{
    %
    Simulation \textit{during} training.
    %
    We evaluate our deformation network with and without simulation loss on a testing sequence.
    %
    It can be seen that penetrations and deformation artifacts are largely reduced when using simulation.
    %
    }
    \label{fig:training_with_sim}
    \vspace{-3mm}
\end{figure}
\vspace{-10pt}
\paragraph{Simulation during Testing.}
We compare our learned cloth deformation with traditional cloth simulation (TCS)~\cite{narain2012adaptive,thomaszewski2008asynchronous,Martin10EBEM} performing sequentially on a test sequence using \textit{PoseNet} to drive the body mesh.
As shown in Fig.~\ref{fig:testing_with_sim}, our results faithfully match the image evidence due to the silhouette constraint.
In contrast, TCS solely provides plausible cloth animation irrespective of the image observation.
Moreover, a single failure in the simulation process is fatal for TCS methods, since they cannot easily recover from such failure due to their sequential nature.
As such, we refrained from reporting quantitative numbers for TCS methods as they failed already after several frames for the green screen evaluation sequences.
In contrast, the presented frame-based approach is robust to failure cases and can recover from bad frames by design.
\begin{figure}[t]
    \centering
    \includegraphics[width=\linewidth]{imgs/ablation_sim.png}

    \caption{
    %
    Simulation during testing.
    %
    Although running simulation directly on the test sequences can lead to stronger wrinkle patterns, these deformations neither match the wrinkles in the image nor the image silhouettes.
    %
    In contrast, our predicted cloth closely matches the silhouettes while also showing plausible deformations.
    %
    }
    \label{fig:testing_with_sim}
    %
\end{figure}

\section{Conclusion}
We propose a physics-aware deep learning-based method for monocular human performance capture.
With physics-based simulation running on the fly as a network layer, we enforce physics plausibility to compensate for the shorthand of using only multi-view images.
We show more visually pleasing results and much-improved physics metrics over state-of-the-art methods.
\vspace{-10pt}
\paragraph{Limitations \& Future Work.}
The simulation layer is not differentiable in our current implementation.
Nevertheless, a fully differentiable physics solver would improve data efficiency and it would open the door to automatic material parameter estimation from video input.
Our method is able to faithfully track body pose and cloth deformations for dynamic input motion, but it cannot produce dynamic effects from a single input image---an inherently ill-posed problem.
To further improve physical fidelity and reconstruction quality, we would like to extend our method to regress dynamically consistent cloth motion by leveraging deep temporal architectures, which take short videos as input instead of single frames.

\section{Acknowledgments}
The authors would like to thank the anonymous reviewers for their valuable feedback, and Gereon Fox for the video narration.
The authors from MPII were supported by the ERC Consolidator Grant 4DRepLy (770784).

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv