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All posts by indisco33
Providence Youth In Peril From Jump
August 24, 2019 indisco33	Leave a comment
PROVIDENCE YOUTH AT PERIL FROM THE JUMP (Published in Providence Journal 8/23/19)
Providence, our Renaissance City, has suffered another black eye with the August 16th assaults and harassment of innocent bystanders by nearly 100 local juveniles. Many of these youngsters were riding stolen Jump rental bikes. Incidents on South Water and Killington Streets plus at India Point included physical assaults, intimidation and retail store shop lifting.
Are we surprised?
We shouldn't be.
Anyone who took the time to read the recent scathing John Hopkins Institute's report on the condition of the Providence Public School System could have predicted this type of behavior in our capital city.
Consider these two excerpts from the report:
" There is widespread agreement that bullying, demeaning, and even physical violence are occurring within the school walls at very high levels, particularly at the middle and high school levels."
"In a large number of classrooms, teachers did not press students to become engaged with the mathematics instructions, resulting in a variety of student off-task behavior: chatting with peers, checking phones, staring into space, or, in some cases, taking phone calls and watching YouTube videos. In some classrooms, this activity was loud enough to disrupt the learning of other students and, in some cases, led to student arguments that left the team concerned for student safety."
This is the environment where Providence's children spend approximately 6 hours of their time for 180 days of the year!
The biblical phrase, "As you sow so shall you reap.", never seemed so sadly applicable.
If high school and middle school children see such lax discipline and disregard for decorum in one of the primary places which are supposed to help shape their future it takes a momentous amount of willpower to navigate unscathed. Statistics show only a heartbreakingly small percentage of Providence public school students move on to higher education.
I am not excusing the horrible actions of the young perpetrators. They chose to organize these near riots using social media, they chose to cut the locks and steal the Jump bikes, they chose to injure people and they chose to commit theft. They should be identified, properly disciplined and then given any available counseling.
My point is simply that the link between the deplorable conditions of the education process (including building conditions) and youthful criminal activity is both obvious and infuriating.
Many of our young students, unfortunately, have impediments to success before they even enter the first grade; generational poverty, family dysfunction, gnawing hunger and lack of good role models. Schools, along with churches, should be beacons of hope and inspiration not further causes of anxiety and fear. I know, and the Hopkins' report agrees, that many individual heroic teachers and administrators strive daily to provide the best education possible despite the obstacles. However, the appalling statistical student failures across the board shown in the report call for draconian actions. Past incremental changes such as new superintendents, classroom restructuring and tweaked curriculums have not stemmed the continual erosion of the system.
New State Education Commissioner Angelica Infante – Green has embarked upon an intensive listening campaign as she gathers information from teachers, students, parents and administrators. On August 8th Infante – Green issued the initial order to begin the process of a state take over of the Providence Public School System for a minimum of three years. While pledging to work with the educational unions, she has allowed that the current contracts need appraising.
These actions have spawned the usual hand wringing and fear mongering from various entities. Upsetting the status quo is often lonely, precarious work.
While state take overs of schools have had varied successes and failures across the nation, Providence's dire situation warrants a full and complete reset from the decades of decline we have all witnessed.
I not only wish Commissioner Infante – Green and our educators success, but also, fervently hope Providence's next generation of students will be able to thrive and get a jump start towards happy, productive lives.
Jim Raftus is a retired marketing executive from Cumberland.
Contact: jraftus@aol.com
John Hopkins ReportJump bikesProvidence Public SchoolsProvidence vandalism
Oppositional Research
I'm now several years retired. Starting to get bored. Thinking of running for public office. My main concern is the increasingly harsh nature of political campaigns. Candidates dive deep into rivals backgrounds looking for dirt. It is now called operational research.
Pondering this first ever candidacy I try to anticipate skeletons buried in my closet. I may have some issues.
Summer of 1955 the manager of the A&P Supermarket on Newport Avenue spots a nine year old me in my first and only attempt at larceny.
"Young man", he scolds "I know your parents. If I ever see you doing this again I'll have to tell them."
To this day driving by this building, even though it is now Hasbro's headquarters, brings back a feeling of guilt.
Spring of 1966 our group of young Rhode Island College students celebrate the first apartment one of us rents off campus. I apparently celebrated with one or two more Miller Hi-Lifes than normal. To keep me out of harms way my accommodating pals placed me in an empty bathtub. THERE MAY BE PICTURES!
Winter of 1966 I dodged the draft, sort of. Strange story. I was drafted. I said goodbyes to friends and family. My normally stoic father teared up as he dropped me off at Fields Point where I was to be sworn in and sent to basic training. While waiting to be given our instructions someone in uniform called out, "Raftus, is there a James Raftus here?".
I was taken into a room and a doctor found a physical issue I knew nothing about and declared me to be 4-F. This was not a day the Army was doing physicals. It was a day to start your Army tour. I did not argue with their decision and returned to civilian life until April of 1968 when the U.S. Army, at this point churning through young men, changed their mind, re-examined me and declared me fit for duty. I enlisted and served until early 1971.
Summer of 1971 was a time to unwind after my Army stint. One evening I was standing on the deck of the former Jamestown Ferry which was docked in Pawtucket having been repurposed as and arts center for the city's youth. I was offered, and accepted, my one and only hit of pot. The only effect it had on me was a mild headache.
Late Spring of 1998 I somehow forgot to pay my 1st quarter of Cumberland taxes and was two weeks late. This was the only time I ever had to pay a fine for local, state, or federal taxes.
I think that's the extent of my personal rogues gallery. How embarrassing would this be to my adult children if it was exposed during the campaign? I can see the headlines from Fox News, "Progressive thief, drunk, draft dodger, drug addict and tax cheat runs for office in Rhode Island!".
But wait, on November 8th of 2016 sixty three million of my fellow citizens voted for a candidate about whom the following was already known:
He had 5 military deferments including, although a self described "great baseball player" at this age, one for questionable bone spurs.
He had suffered multiple failed business ventures ending in bankruptcies.
His real estate organization was sued by the U.S Department of Justice in 1973 for racial discrimination.
He had cheated on all 3 of his wives, once, supposedly, with a porn star shortly after the birth of his son.
He was the first candidate in decades to not release his tax returns.
He bragged about grabbing women by their private parts.
Well, maybe, just maybe voters could forgive my sordid past. Yes, I just may toss my stained hat into the ring. Oppositional research be damned!
What should I shoot for, Town Council, School Board or something more ambitious?
Jim Raftus lives in Cumberland and is not running for anything.
CandidateelectionspoliticsscandalsTrump
Closing The Church
CLOSING THE CHURCH 1.
Because I've not been a regular church goer for almost five decades I reluctantly consented to attend the final Sunday mass at the soon to be shuttered St. Leo's in Pawtucket. My reluctance was based on the fact that I knew attendance for this mass would be overflowing and I felt current parishioners and long time St. Leo's attendees should not lose a seat because of me. However my sister, Virginia, who has been faithful to her faith, no longer drives and needed a ride.
St. Leo's is a small church and all the familiar icons were on display as we entered; the side baptismal font presided over by a statue of the church's patron saint, the pink ceilings with holy scenes painted in rectangular patterns and the Virgin Mother standing guard over the votive candles.
Five minutes before the start of the service the pews were packed, a very rare occurrence these past years. I sensed sadness leavened with inevitability in the mostly elderly gathering as Reverend Michael Sisco, escorted by altar girls and past parish priests, began the opening processional.
St. Leo's had an outsized influence on the Darlington section of Pawtucket where I grew up. My family's history ran deep. My brother, sister and I all attended St. Leo's School next to the Church. Raftus baptisms, first communions, confirmations, weddings and funerals were all held in this Church. A memorial brick honoring our parents is part of a collection in front of the entrance.
Although I felt a stranger in the midst of these more diligent worshipers
I also felt a sense of peace as the familiar rituals of the mass unspooled. Prior to the mass my sister, wife, sister- in -law and I lit candles in memory of my older brother Mike who passed this past October. I took great solace in that. My wife and I also found joy, and surprising sentiment, watching the priest, Father Davenport, who married us on this altar forty four years ago return to help celebrate the mass.
For a reluctant attendee, a lapsed Catholic, I was experiencing a flood of emotions in this old building.
St. Leo's Parish was established in 1916 and flourished for decades. In typical Rhode Island fashion it served a mainly Irish contingent while St. Cecilia's Church barely three hundred yards down the street had a predominately French clientele. Attendance at both places of worship waned starting in the 1990's. St. Leo's was forced to close their school in 2008. In 2011 the decline in parishioners and income necessitated the merging of these two parishes into a new entity called the parish of St. John Paul II, a solution which caused angst on both sides as closure of one church seemed unavoidable. After much speculation the announcement of St. Leo's closing became official in December of 2018.
Reverend Sisco, who has served as pastor for both churches the past few years, deftly used his homily at this final Sunday mass to mix sentiment with hope. He also leaned heavily on the beautiful sentiments of a letter written to him for the occasion by Sister Marie Fatima (Nunes) O.P., a long time parishioner who penned a powerful homage to the church's past and to the endurance of faith which transcends any one place of worship. Her words, read by Reverend Sisco, reminded all that it is not the walls, the columns or the choir loft which keeps a religion intact, it is the faith of the people.
This last Sunday gathering was the mass of the Epiphany which celebrates the three magi offering of gifts to the baby Jesus. While there was no gift of a great personal conversion for me in my final hour in St. Leo's I did feel a palpable resurgence of faith which may mean less reluctance to make occasional visits to places of worship of varied denominations to recapture that sense of shared community.
Funny how even a brother's reluctant favor can restore one's faith.
CatholicReligionSt. Leo's
A 9/11 Legacy (Published in the ProJo) 2018
September 10, 2018 indisco33	Leave a comment
A 9/11 LEGACY
Seventeen years ago on September 11th the world lost the energy, potential and love of 2,977 souls. The families of those killed knew of their dreams as well as their accomplishments. Untold good works and unfulfilled ambitions all also died that tragic day.
Shakespeare in Julius Caesar famously wrote, "The evil men do lives after them; the good is oft interred with their bones."
On this anniversary of that horrid day I feel both obliged and privileged to dispute the Bard's ominous warning.
We lost my cousin Rhode Island native David Angell and his dear wife Lynn on American Airlines Flight #11. I have chronicled David's success as a television writer and producer and Lynn's incredible service and volunteer work at The Hillsides Center for Children in California in an earlier Providence Journal commentary. (September 11, 2013.)
After several years of failed attempts at cracking into the very difficult television industry David eventually broke through and in fact with his work on Cheers, Wings and Frasier ascended to the highest level of achievement as witnessed by his 8 Emmy Awards. Frasier became the most profitable comedy show in television history. David and Lynn reaped the benefits and in 1996 created the Angell Foundation and quietly began their amazing charitable journey. So under the radar was their philanthropy that even though David and I were peers and close first cousins who had many adventures together as he grew up in Riverside and I lived in Pawtucket, I knew nothing about the Angell Foundation until after their deaths.
Our family was last with them three days before their plane was hijacked. Frasier was still at the top of the ratings, but, David and Lynn told us that while he wasn't retiring, he and his two partners had just structured a new schedule so that David would have to spend far less time in California. You see, David and Lynn's real love was with New England; a condominium in Stowe, Vermont, a new home nearing completion on Cape Cod in Chatham and a town house on College Hill in Providence a city they truly cherished.
David was a proud member of the Providence College Class of 1969 and often worked closely with the school on special projects such as the Angell Blackfriars Theatre which was posthumously named after David and Lynn upon opening in 2005.
David and Lynn's love of New England continues to be reflected in the grants awarded in their names by the ongoing Angell Foundation efforts. The Foundation focuses on assisting worthy organizations in just two regions; Southern California and New England. For the fiscal year ending this past June, nearly 17 years after their deaths, the Angell Foundation still awarded $630,000 to New England causes. This includes $175,000 to the Rhode Island Community Food Bank, $30,000 to Farm Fresh Rhode Island and $100,000 to Rhode Island School of Design's Project Open Door which assists underserved teenagers in the state's urban core cities. In fiscal 2017 the New England initiatives received a whopping $1,220,000 in total grants.
Every year as the calendar reaches early September a sense of loss and sadness naturally comes over David and Lynn's extended families as well as the other nearly 3,000 families affected. Our sorrow, however, is greatly assuaged as we reflect on the fact that David and Lynn's generous spirits live on through the great work of the Angell Foundation they quietly established so long ago.
As a counterpoint to Shakespeare's rather dark "evil lives on…" quote I'll end with this from his 12th Night, "Some men are born great, others achieve greatness."
9/11Angell Foundation
APEX REMEMBERED
September 3, 2018 indisco33	Leave a comment
The late Norman Fain, one of the principal owners of the Apex stores, would only occasionally visit his properties. Most times it would be at night about an hour or so before closing. Mr. Fain was a quiet, unassuming man and I think very few of his employees even knew who he was as he wandered the aisles dressed in a conservative, grey suit with a fedora perched on his head.
"Good evening, Mr. Fain." I said as he entered the major appliance department in his downtown Pawtucket store where I worked part time after my Army discharge in 1971 and my return to college.
I continued, "I had couple in today who said they were great friends of yours."
Mr. Fain half smiled, looked at my name tag, there was no reason he'd know my name, and replied, "Well, Jim, if I had all the friends who claim they are my friends when they come here to buy something, I'd be broke by now."
"Keep up the good work." he intoned as he headed for the garden shop.
These days I think of this, and many other memories, every time I drive past the rusting pyramid, the stain splotched facade and the unkempt grounds of the present, sad iteration of the once iconic Apex Pawtucket store.
My family, and I suspect many other local families, had a long history tied in with the Apex store evolution. Both my parents worked at the original Central Avenue location.
My Father in the appliance department (apple and tree analogy) and my Mother as an office assistant in the 1960's.
My first job when I became old enough to work was in the product pick up warehouse. One of our many duties was bicycle assembly. To break the monotony of this boring task we devised a "test track" to assure the quality of our work. We built multiple ramps by angling shipping pallets propped up on mattresses and we'd ride pell mell over this course with tires squealing on the concrete floor. One afternoon a much older supervisor, perhaps back from a liquid lunch, ramped things up by constructing a circle of fire for us to jump through! So, if you received a singed bike for Christmas from Apex in the 1960's now you know why.
Despite these shenanigans I was promoted and at 17 wound up as a salesman in the men's clothing department. I was a very shy teenager and my first customer was a stunningly beautiful young woman who I guessed to be in her early twenties. I was so tongue tied I barely was able to answer her questions. She was buying an assortment of shirts and ties for her boyfriend. I was bedazzled! Who knew such angelic creatures existed? My nerves were so frayed during this, my first retail sales transaction, I could not even get her selection into the Apex shopping bag. With profound patience, and I believe a bit of humor, she helped me arrange the gifts to fit the bags.
During the next few years the Apex empire expanded with new stores and I managed to gain some maturity and much needed confidence. College and Uncle Sam intervened and yet, as noted above, I once again turned to Apex after my Army stint for a perfect part time job while finishing college. In fact it was while working in the major appliance department of the Warwick location that I was recruited by a wholesale distribution company and a long, satisfying marketing and sales career was launched.
So the dilapidated Pawtucket shell of my former employer, much in the news this past year, saddens me. The sorrowful condition allowed by present owner Andrew Gates, grandson of the Apex corporation founder Albert Pilavin, remains a blemish which makes more difficult the daunting task of revitalizing Pawtucket. Larry Lucchino, CEO of the Pawtucket Red Sox recently described Mr. Gates as, " ..a stubborn owner."
The Apex stores were always impeccably maintained, customer friendly and the Fain family were generous philanthropist. All of this is now destroyed. Mr. Fain, who passed
in 2003, would not be pleased. Neither are the many former employees of this once cherished institution.
The ballpark proposal failed. The city is talking about purchasing the Apex site for future development. Let's hope the parcel becomes something to be proud of and the obstinance of one man does not prevail.
Now, here's the truth about the real friends of Mr. Fain. If a sales associate at an Apex store noticed the numeral "3" as the third digit of the Apex credit card that signified a true friend of Fain and no credit checks were needed!
Jim Raftus, a retired marketing executive, lives in Cumberland.
Contact at jraftus@aol.com
APEXPAWTUCKET


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text:
\section{Introduction}
\label{sec:intro}
Recently, 3D face reconstruction from single ``in-the-wild'' images has been a very active topic in Computer Vision with applications ranging from realistic 3D avatar creation to image imputation and face recognition \cite{wu2020unsupervised, gecer2019ganfit, tewari2018self, lattas2020avatarme, ruiz2020morphgan, egger20203d}. Nevertheless, despite the improvement in the quality of the 3D reconstructions, all of these methods do not accommodate any statistical variations in the oral cavity let alone a tongue template mesh. As a result, the oral region is completely disregarded from the final result.

Being able to reconstruct the tongue expression has multiple advantages in various applications. First of all, the generated avatars would be more realistic and would be able to mimic many more facial expressions. Moreover, speech animation tasks would be improved as the inclusion of the oral cavity plays a significant role. Finally, face recognition applications could be enhanced as more extreme poses and expressions would be modeled.

However, as we already pointed out, all of the current state-of-the-art (SOTA) methods \cite{wu2020unsupervised, gecer2019ganfit, tewari2018self} do not contain the tongue component in their implementations. This is because of two reasons: a) there is no publicly available tongue dataset, and b) it is very challenging to carry out 3D reconstruction of the face together with the tongue in ``in-the-wild'' conditions, because of the highly deformable nature of the human tongue.

To tackle the absence of tongue data, we collected a large and diverse dataset of textured 3D tongue point-clouds (more info about the data in Section \ref{sec:tongue_recon}). Having captured the data, we created a pipeline which is comprised of the following parts: a) a tongue point-cloud autoencoder (AE) which is used to derive useful 3D features of our raw collected 3D data, b) a tongue image encoder optimized based on the aforementioned 3D features, c) a shape decoder which translates the encoder outputs to the parameter space of the Universal Head Model (UHM) \cite{ploumpis2020towards}. We should note that the UHM in our case is further rigged/modified so that it can model various tongue shapes/expressions, as explained in Section \ref{sec:tongue_recon}. We begin by training the AE in step a) and then we train steps b-d) in an end-to-end fashion so that the output tongue expression of the UHM model is as close as possible to the corresponding ground-truth 3D tongue point-cloud of the 2D tongue image.

Since there is a lack of ground-truth 3D tongue data corresponding to ``in-the-wild'' 2D tongue images, the pipeline we described so far is only trained using our collected data which were captured under controlled conditions. This results in sub-optimal performance in ``in-the-wild'' conditions. To remedy this, we developed a novel conditional GAN framework that is able to generate accurate 3D tongue point-clouds based on the image encoder outputs (step b) of the pipeline). Having created new image/point-cloud pairs of ``in-the-wild'' tongue data, we re-train the pipeline using also these new data. As we show in Section \ref{sec:experiments}, this addition substantially improves the quality of the tongue reconstructions.

To summarize, the contributions of our work are the following:

\begin{itemize}
    \item We release a dataset of $1,800$ raw tongue scans of various shapes and positions, corresponding to around $700$ subjects. Being the first such diverse tongue dataset, it can be proven very useful to the community.
    \item We present a complete pipeline trained in an end-to-end fashion that is able to reconstruct the 3D face together with the tongue from a single image.
    \item To make this pipeline robust to ``in-the-wild'' images, we introduce a novel GAN framework which is able to accurately reconstruct 3D tongues from ``in-the-wild'' images with an increasing level of detail.
\end{itemize}

\begin{figure*}[t]
\begin{center}
\includegraphics[width=1\linewidth]{main_figs/synthetic_data3.pdf}
\caption{Random 3D tongue expressions of our synthetic database based on the mean UHM template. The expressions are rigged and manually sculpted to induce more variance around the tongue surface and the general oral cavity.}
\label{fig:synthetic_data}
\end{center}
\end{figure*}

\section{Related Work}
\label{sec:Related_work}
Single view 3D reconstruction of human facial/head parts is undeniably an extremely valuable task in Computer Vision. However, it has posed many challenges to the research community, due to the fundamental depth ambiguities and the ill-posed nature of the problem. In order to constrain the ambiguity of the problem, many statistical parametric models have been introduced for different parts of the human face/head \cite{blanz1999morphable,booth20163d,li2017learning,ploumpis2019combining}.

Due to the increasing interest of facial analysis over the years the research community has mainly focused on human facial reconstructions. Since the inception of facial 3D Morphable Models (3DMMs) in \cite{blanz1999morphable}, a myriad of scientific papers have been published focusing solely on the reconstruction of facial shape and appearance \cite{booth20173d,booth20183d,gecer2019ganfit,lattas2020avatarme}. Only recently with the emergence of 3D scanning data has the research interest shifted to other significant parts of the human head. A few head models have been introduced during the recent years but without any statistical craniofacial correlations \cite{li2017learning, ranjan2018generating}. The first craniofacial 3DMM of the human head was introduced  in \cite{dai20173d} and later extended and leveraged into a 3D head reconstruction setting from unconstrained single images \cite{ploumpis2019combining}. A few recent works tried to align a skull structure of the the human head with the facial topology \cite{liu2018superimposition, madsen2018probabilistic} in order to obtain a distribution of plausible face shapes given a skull shape.

Finer details of the human face/head started to appear with the introduction of 3D human ear modeling \cite{zhou2017deformable}. Ears are key structures of the human head that have an important contribution to the biometric recognition and general appearance of a person. The two foremost examples of ear models were introduced in \cite{zolfghari2016ear,dai2018ear} but none of them was fused to a face/head in order to create a complete appearance.

Moreover, in an attempt to overcome the ``\emph{uncanny valley}'' problem, a few approaches have tried to model the independent variations/movements of the human eye and the the facial eye region \cite{wood20163d, berard2016lightweight}. These efforts are challenging due to the limited amount of data around the eye region and the extreme level of detail required for this task. Moving towards the oral cavity, teeth modeling was introduced in \cite{wu2016model,velinov2018appearance}, where the 3D structure of the teeth was recovered from 2D images via an elaborate optimization scheme.

Only very recently, a few approaches \cite{li2020learning, ploumpis2020towards} have tried to combine all of these aforementioned attributes of the human head (eyes, ears, teeth, and inner oral cavity) in order to build a complete model in terms of shape and texture, which accurately represents the human head. Although these models include an oral topology, none of them deals with the dynamics of the tongue, something which is really important for speech animation and the overall realness of the avatar representation. To this end, in this work we aim at extending these approaches and paving the way towards a realistic human appearance by releasing a diverse 3D tongue dataset to the research community. We also present the first framework for accurate 3D human tongue reconstruction from single images.

\begin{figure*}[t]
\begin{center}
\includegraphics[width=0.90\linewidth]{main_figs/method_3.1.pdf}
\caption{An illustration of our tongue reconstruction framework. First we train the point-cloud AE on its own to get meaningful 3D features ($\mathbf{y}$) and then we trained the image encoder /shape decoder using a number of different losses as explained in Section \ref{sec:tongue_recon}}
\label{fig:method}
\end{center}
\end{figure*}

\section{3D human tongue reconstruction}
\label{sec:tongue_recon}
In this Section we present the complete tongue reconstruction pipeline. We begin by describing our collected 2D/3D tongue dataset and our manually rigged tongue dataset which is based on the the UHM \cite{ploumpis2020towards} template. We further provide details about the point-cloud AE, the image encoder, the shape decoder and the overall loss functions we used to optimize the pipeline for the tongue reconstruction. Moreover, we present the novel conditional GAN method which is able to accurately reconstruct 3D tongue point-clouds of ``in-the-wild'' tongue images. Finally, we explain how we used the generated point-clouds of the GAN to re-train the pipeline to achieve better results in ``in-the-wild'' conditions.

\subsection{Tongue datasets}
\label{sec:datasets}
\textbf{TongueDB: the first 3D tongue dataset.}
As mentioned in Section \ref{sec:intro}, we collected a large dataset comprising of textured 3D tongue scans. Our point cloud database, dubbed TongueDB, contains approximately 1,800 3D tongue scans which were captured during a special exhibition in the Science Museum, London. The subjects were instructed to perform a range of tongue expressions (\textit{i}.\textit{e}., tongue out left and right, tongue out center, tongue out center round, tongue out center extreme open mouth, tongue inside left and right, etc.). Some example images can be seen in Fig. \ref{fig:raw_dataset}. The capturing apparatus utilized for this task was a 3dMD 4 camera structured light stereo system, which produces high quality dense meshes. We recorded a total of 700 distinct subjects with available metadata about them, including their gender ($42\%$ male, $58\%$ female), age, and ethnicity ($82\%$ White, $9\%$ Asian, $3\%$ Black and $6\%$ other).

\textbf{Rigged tongue database.}
In order to carry out 3D tongue and face reconstruction, we would need to use a face/head model. Nevertheless, one major drawback of all of the currently used face/head models \cite{li2017learning,ranjan2018generating,dai20173d} is that they are missing the tongue component. This is because it is a challenging task to non-rigidly capture in a fixed template the 3D topology of the oral cavity. These challenges include: a) the highly deformable nature of the tongue, b) the non-convexity of the mouth region, c) the specular texture of the teeth. In order to alleviate this issue, we constructed a synthetic 3D head and tongue dataset rigged by 3D artists. For our neutral mesh template $\mathbf{\bar{T}}$ we utilize the \textsl{mean template} of the UHM \cite{ploumpis2020towards} as it provides all the necessary components of the human oral cavity in accordance with the entire head statistical structure. The resulting rigged tongue expressions rise at $75$ distinct meshes. In order to further augment our synthetic dataset, we performed trilinear interpolation between the closest expression meshes and generated a total of $n_s=720$ tongue expressions. Some example synthetic expressions are shown in Fig. \ref{fig:synthetic_data}. A standard PCA was applied on the interpolated meshes resulting in an orthogonal basis matrix $\mathbf{U}_t \in \mathds{R}^{3N\times n_t}$ (where $N$ are the mesh vertices and $n_t=110$ the kept components). The PCA is performed on the entire set of head vertices and not solely on the oral cavity. In this way, it is more efficient afterwards to transfer the tongue expression from the mean head to a head with a different facial identity.

\subsection{Method}
\label{sec:method}
\textbf{Tongue point-cloud AE.}
In order to accurately reconstruct a tongue in its 3D form based on a 2D image, our image encoder needs to be guided by meaningful target labels which can capture all the desired 3D point-cloud information. These labels, denoted as $\mathbf{y}\in\mathbb{R}^{256}$, are learned by autoencoding the raw point-clouds of our dataset (\textit{i}.\textit{e}., the raw 3D tongue scans). For this task, we utilize a self organizing-map framework for hierarchical feature extraction \cite{li2018so}.

\textbf{Tongue image encoder.}
\label{sec:emb_net}
The task of the tongue image encoder is to produce features which are close to the target 3D features $\mathbf{y}$ of the AE. To make the encoder robust to various camera angles or illuminations, we employ a rendering framework where we utilize the textured raw scans (TongueDB in Section \ref{sec:datasets}). We render our 1,8K textured meshes with a pre-computed radiance transfer technique using spherical harmonics which efficiently represent global light scattering. Additionally, we use more than 15 different indoor scenes coupled with random light positions and mesh orientations around all 3D axes, resulting in approximately 100K images. As an encoder we used a ResNet-50 \cite{he2016deep} model pre-trained on ImageNet \cite{deng2009imagenet} and fine-tuned it on our dataset. In particular, we modified the last layer of the network to output a vector $\tilde{\mathbf{y}} \in \mathbb{R}^{256}$ similar to the dimension of the ground truth vector $\mathbf{y}$.

\textbf{Shape decoder.}
In order to decode the encoder $\tilde{\mathbf{y}}$ labels into meaningful tongue shapes, we use the synthetic PCA model $\mathbf{U}_t$ of the rigged tongue expression dataset. To this end, after producing the $\tilde{\mathbf{y}}$ labels, we utilize a standard multi-layer perceptron (MLP) which works as a regression scheme to the latent parameters $\mathbf{p}_t \in \mathds{R}^{110}$ of the synthetic PCA tongue model. The statistical nature of the PCA model helps us constrain the final result during training and ensures meaningful deformations which lie inside the spectrum of our rigged/modified UHM model.

\textbf{Pipeline training}.
During training, we first train the point-cloud AE on its own and then train the pipeline of the image encoder and shape decoder in an end-to-end fashion. To optimize the pipeline, we apply a total of $6$ losses with each one contributing to the quality of the final result. The first $2$ losses are calculated between the predicted tongue expression of the rigged/modified UHM model and the ground-truth tongue point-cloud of the corresponding input image. Similarly to \cite{wang2018pixel2mesh}, we adopt a Chamfer loss \cite{fan2017point} $\mathcal{L}_{CD}$ to optimize the position of the resulting template points as well as a normal loss $\mathcal{L}_{n}$ to correct the orientation of the mesh. In order to compute an accurate Chamfer loss, we only utilize a small area around the oral cavity which is defined based on the ground-truth point-cloud. Additionally, we calculate a Laplacian regularization $\mathcal{L}_{l}$ loss between our predicted mesh the mean shape of the PCA model in order to prevent the vertices from moving too freely outside the mean positions and constrain the resulting shape to be smooth. An edge length loss $\mathcal{L}_{e}$ is also introduced which penalizes any flying vertices (outliers). Finally, we employ a collision loss $\mathcal{L}_{c}$  which prevents the points of the tongue to penetrate the surface of the oral cavity and is formulated as the sum of each collision error around the $12$ mouth landmarks of the UHM template (as illustrated in the supplementary material):

\begin{equation}
\mathcal{L}_{col}=\frac{1}{N} \sum_{k=0}^{11}\sum_{i=0}^{N-1}\max\left(0, d_{k}^i\right)
\end{equation}
\[d_{k}^i = r^2 - \left(q_{1}^i - x_k\right)^2 - \left(q_{2}^i - y_k\right)^2 - \left(q_{3}^i - z_k\right)^2\]

\begin{figure*}[h!]
\begin{center}
\includegraphics[width=1\linewidth]{main_figs/net_diagram_3.pdf}
\caption{Symbol $\mathbf{c}$ stands for row-wise concatenation along the channel dimension. Symbol $\mathbf{o}$ stands for element-wise (\textit{i}.\textit{e}., Hadamard) product. The Generator inputs are a Gaussian noise sample $\mathbf{z}$ and a label $\mathbf{y}$ corresponding to a particular tongue, from which we want to sample a 3D point. The Discriminator input pairs are a label $\mathbf{y}$ which corresponds to a specific tongue and $\mathbf{x}_t$ a \textsl{real} 3D point belonging to the aforementioned tongue point-cloud (but sampled as explained in Section \ref{sec:novel_loss}) and $G\left(\mathbf{z}, \mathbf{y}\right) = \tilde{\mathbf{x}_t}$ a generated point belonging to this tongue. The Discriminator is asked to distinguish the real from the fake point.}
\label{fig:net_diagram}
\end{center}
\end{figure*}

The $\mathcal{L}_{col}$ is calculated as the sum of distances of each collided point $\mathbf{q}^i=\{q_{1}^i,q_{2}^i,q_{3}^i\}$ to the sphere $k$ with center the landmark coordinates $x_k, y_k, z_k$ and radius $r=1,5cm$.

Lastly, we impose a final L2 loss $\mathcal{L}_{y}$ in the intermediate step of our pipeline where we constrain the predicted $\tilde{\mathbf{y}}$ encoded features to be as close as possible to the ground-truth features ${\mathbf{y}}$ of the corresponding autoencoded point-cloud. This loss is of paramount importance because: a) the $\tilde{\mathbf{y}}$ features in this way contain rich 3D information invariant to texture/illumination variations and b) our ``in-the-wild'' extension which we introduce later is based on a generative point-cloud framework that relies on such rich 3D features.

The final loss function $\mathcal{L}_{total}$ is given by:
\begin{equation}
\small{
    \mathcal{L}_{total} =  \lambda_1\mathcal{L}_{CD} +   \lambda_2\mathcal{L}_{n} + \lambda_3\mathcal{L}_{l} + \lambda_4\mathcal{L}_{e} + \lambda_5\mathcal{L}_{c} +
     \lambda_6\mathcal{L}_{y}
     \label{eq:total_loss}}
\end{equation}

where $\lambda_1, \ldots, \lambda_6$ are training hyper-parameters. During inference, the encoder network takes as an input a single tongue image and predicts a 3D embedding $\mathbf{\tilde{y}}$, which is later transformed to the corresponding $\mathbf{p}_t$ parameters of the synthetic expression model, through the MLP between the two latent spaces. Finally, we apply the PCA model of the rigged head model on these $\mathbf{p}_t$ parameters to derive the final mesh of the head with the tongue expression. An overview of the methodology can be seen in Fig. \ref{fig:method}.

\subsection{TongueGAN for ``in-the-wild'' reconstruction}
\label{sec:gan}
Although the pipeline presented in Section \ref{sec:method} provides a good estimation of the tongue pose in the test set of our collected data, it does not perform very well in ``in-the-wild'' images (Fig. \ref{fig:qual_itw_test}). This behavior is expected because our collected data were captured in controlled conditions and the training of the encoder was carried out only with rendered images which do not fully mimic ``in-the-wild'' conditions. To make our approach robust in ``in-the-wild'' images too, we would need to further train the pipeline by using also such data. However, for ``in-the-wild'' collected images from the web, we do not have their corresponding 3D tongue point-clouds. As a result, to use ``in-the-wild'' data in the pipeline, we would first need to have a method that can learn the distribution of our collected 3D tongue data and generalize well.

Finding a method to generate novel 3D tongues is tricky. This is because of several unique properties of the human tongue: a) it is a highly deformable object, so we cannot register our collected data in a reference template and apply relevant methods \cite{bouritsas2019neural,moschoglou20193dfacegan,potamias2020learning}, b) it is a non-watertight surface (i.e., it contains holes) so we cannot also use any implicit function approximations methods \cite{park2019deepsdf, saito2019pifu, michalkiewicz2019deep} or volumetric approaches \cite{jackson2017large,jackson20183d,zheng2019deephuman}. Therefore, having excluded the aforementioned categories, we decided to use GANs \cite{goodfellow2014generative} for the 3D tongue surface generations.

In order to generate accurate point-clouds that correspond to certain tongue images, our GAN, dubbed as TongueGAN, needs to be guided by meaningful labels which can capture all the desired 3D surface information. These labels are provided by the trained point-cloud AE as described in Section \ref{sec:tongue_recon}. Since the generation is driven by labels, TongueGAN is a conditional one \cite{mirza2014conditional}.

In particular, given a label denoted as $\mathbf{y}$ and a random Gaussian noise $\mathbf{z}\in\mathbb{R}^{128}$, the generator $G$ produces a novel point-cloud \textsl{point} $G\left(\mathbf{z}, \mathbf{y}\right)\in\mathbb{R}^{3}$, which we denote as $\mathbf{\tilde{x}}_t$, that belongs to the tongue surface represented by the label $\mathbf{y}$. On the other hand, the discriminator $D$ receives as inputs the label $\mathbf{y}$, a real point-cloud point $\mathbf{x}_t$ (which belongs to the tongue represented by the label $\mathbf{y}$) and the generator output $\mathbf{\tilde{x}}_t$ and tries to discriminate the fake (\textit{i}.\textit{e}., generated) from the real point. In the mathematical parlance, this can be described as:

\begin{align}
      \begin{array}{ll}
        \mathcal{L}_D = \mathbb{E}_{\mathbf{x}_t}\left[\log D\left(\mathbf{x}_t, \mathbf{y}\right)\right] - \mathbb{E}_{\mathbf{\tilde{x}}_t}\left[\log D\left(\mathbf{\tilde{x}}_t, \mathbf{y}\right)\right],\label{eq:adv_loss}\\
          \mathcal{L}_G = \mathbb{E}_{\mathbf{\tilde{x}}_t}\left[\log D\left(\mathbf{\tilde{x}}_t, \mathbf{y}\right)\right]
        \end{array}
\end{align}

where $D$ tries to maximize $\mathcal{L}_{D}$, whereas $G$ tries to minimize $\mathcal{L}_{G}$. The training process is considered complete when $D$ is no longer able to differentiate between the real and fake point-cloud points.

Please note that instead of generating whole point-clouds for every provided pair $(\mathbf{z}, \mathbf{y})$ of noise and label, respectively, we merely generate a point corresponding to the surface which the label $\mathbf{y}$ represents. That confers several advantages in comparison to the rest of the methods in the literature, such as: a) we do not need to have in our training set point-clouds with the same number of points and as a result we can train our GAN without any data pre-processing on the raw point-clouds, which do not have a fixed number of points among them, b) when it comes to generating point-clouds corresponding to a particular label, we can generate on demand as many points as we want and, contrary to the rest of the literature, we are not constrained by any initially fixed resolution.

Since their inception, a plethora of GAN architectures and losses have been introduced in the literature \cite{arjovsky2017wasserstein, gulrajani2017improved, chen2016infogan, cheng2019meshgan, mao2017least}. For the TongueGAN loss we chose the Wasserstein loss with Gradient Penalty (WGAN with GP) \cite{gulrajani2017improved} due to its stability and good performance throughout the training process. As far as the architecture is concerned, we turned our attention to the recently proposed $\Pi$-Nets \cite{chrysos2020p, chrysos2020deep}, which are easy to implement and achieve state-of-the-art results in a large battery of tasks, including graph representation learning. In particular, we use our own, custom modification of $\Pi$-Nets to accommodate the needs of our task. A graphical presentation of the network structure is provided in Fig. \ref{fig:net_diagram}.

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.85\linewidth]{main_figs/margin_points.pdf}
\caption{A visual description of the mechanism explained in Section \ref{sec:novel_loss}. In a) we depict a raw tongue mesh with blue colour and with green colour we draw a section. In b) we show the 3D points (green color) that belong to the tongue surface and lie upon the section and with lighter green we depict the accepted areas from which points can be sampled and then fed to the discriminator as real data. These lighter green areas become gradually smaller as the training evolves till they collapse to the ground-truth 3D points. In c) we provide a zoomed-in area. }
\label{fig:margin_points}
\end{center}
\end{figure}

\subsubsection{GAN loss for accurate surface approximation}
\label{sec:novel_loss}

Even though, as can be clearly seen in the experiments, our custom $\Pi$-Nets modification along with the WGAN-GP loss significantly improve upon the vanilla GAN or point-cloud GAN \cite{li2018point}, there is still room for improvement as the point-cloud representations are not ideally reconstructed (see Table \ref{tab:errors}).

We primarily attribute this to the strict behavior of the discriminator in GANs (\textit{i}.\textit{e}., deciding in our case whether a generated point matches exactly a point of the target point-cloud). This rigidity, especially in the early steps of the training process, is not very helpful, as the generator struggles to learn the real distribution of the point-clouds (\textit{i}.\textit{e}., all of the generated points are discarded as fake by the discriminator with high confidence).

To remedy this, we slightly softened the discriminator, especially in the initial steps, by slightly modifying the real points fed to it. To achieve this, instead of directly feeding a real point $\mathbf{x}_t$ corresponding to a label $\mathbf{y}$ to the discriminator, we feed the following:

\begin{align}
\mathbf{x}_{t'|\mathbf{y}} \sim \mathcal{N}\left(\mathbf{x}_{t|\mathbf{y}}, \sigma_{e}\mathbf{I}
\right)\label{eq:sampling}
\end{align}

where $\mathcal{N}\left(\mathbf{x}_{t|\mathbf{y}}, \sigma_{e}\right)$ is a multi-variate normal distribution with mean $\mathbf{x}_t$ and (isotropic) variance $\sigma_e$. The variance $\sigma_e$ is not dependent on the label $\mathbf{y}$. It is only dependent on the epoch $e$. By employing \eqref{eq:sampling}, especially when the training process commences, the generator can better learn the actual distribution as it does not get severely punished by the discriminator when it slightly misses out the actual surface (see the accompanying Fig. \ref{fig:margin_points} for a visual understanding). As can be also seen in the experiments, this addition yields better results and stabilizes the training even further. We begin the training with a relatively small value for the variance and further linearly reduce it as we go along with the training process till it basically becomes zero towards the final epochs. This is further corroborated empirically in Section \ref{sec:experiments}.

\subsubsection{Re-training the pipeline}
\label{sec:retraining}
After the training is complete, we use the trained generator together with the trained encoder from Section \ref{sec:method}. In this way, we create ``in-the-wild'' pairs of 2D/3D data as follows: we feed the 2D ``in-the-wild'' image to the encoder and get the label $\tilde{{\mathbf{y}}}$. We then use this label $\tilde{{\mathbf{y}}}$
and the generator to produce a 3D point-cloud of the input image. As we can see in Fig. \ref{fig:itw_qual}, although TongueGAN is trained only on our collected data, it is able to generalize very well in ``in-the-wild'' images and as a result we can use it to create 2D/3D tongue pairs. We apply this process to a number of ``in-the-wild'' images to create multiple pairs. Finally, we re-train the pipeline we presented in Section \ref{sec:method}, using also the aforementioned pairs.

\section{Experiments}
\label{sec:experiments}
This Section is organized as follows. We begin by providing details regarding the training of the networks. Moreover, in Section \ref{sec:control}, we outline a series of quantitative as well as qualitative experiments under control conditions and finally, in Section \ref{sec:ITW} we describe our results under ``in-the-wild'' images.

The MLP utilized for the regression between the labels $\tilde{\mathbf{y}}$ and the PCA parameters $\mathbf{p}_t$ in Section \ref{sec:method} has a structure of (256, 128, 110) with a ReLU activation in the intermediate layers. The hyper parameters of $\eqref{eq:total_loss}$ which balance the losses are $\lambda_1=1.2$, $\lambda_2=1.6e-4$, $\lambda_3=0.4$, $\lambda_4=0.2$, $\lambda_5=0.8$ and $\lambda_6=1.5$. As described in Section \ref{sec:gan}, for TongueGAN we used a variant of WGAN with GP \cite{gulrajani2017improved}, which includes the injection mechanism \cite{chrysos2020p}, as well as the surface loss function presented in Section \ref{sec:novel_loss}. More specifically, we utilized a 9-layer Generator ($G$) and a 8-layer Discriminator ($D$) with a total number of parameters of about $8\times 10^6$ and $4\times10^6$, respectively. We trained TongueGAN using the Adam optimizer \cite{kingma2014adam} with $\left(\beta_1=0,\beta_2=0.9\right)$. We also trained with a batch size of $2048$ for a total of $10^6$ iterations. Following the idea introduced in \cite{heusel2017gans}, we use individual learning rates for D and G with values of $1e-4$ and $1e-5$, respectively. Finally, we start training with the variance $\sigma_e$ in $\eqref{eq:sampling}$ being $5e-3$ and we linearly decrease it by $10\%$ every $50\times10^3$ steps. The exact network structures are deferred to the supplementary material with more details.

\subsection{3D tongue reconstruction in control conditions}
\label{sec:control}
In this set of experiments, we used $90\%$ of TongueDB for training and the rest for testing. Due to the intricacy of the tongue as a surface (as we explained in detail in Section \ref{sec:gan}), we decided to use a GAN for the tongue surface generation part. Moreover, an extra reason to utilize a GAN for the training is the fact that it is able to generalize very well in unseen labels during testing. To the best of our knowledge, the only method which has been introduced in the literature and is able to carry out point-cloud generations based on unseen labels is PointCloud GAN (PC-GAN) \cite{li2018point}. Consequently, in what follows we draw comparisons against PC-GAN \cite{li2018point} and another two variants of TongueGAN, namely: a) TongueGAN\_v1, which is the same as TongueGAN with the only difference being that the novel loss function (Section \ref{sec:novel_loss}) is not available in this version, and b) TongueGAN\_v2, which is the typical GAN structure where, instead of the injections we have simple concatenations along the layers. Finally, we also report the results for the regressed tongue expression (referred to as Tongue-Reg). For this, we only take into account a small patch around the oral cavity defined by the ground truth point cloud, in order to deduce a reasonable error.

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.95\linewidth]{main_figs/raw_point_cloud_dataset_2.pdf}
\caption{Various raw 3D tongue scans of our database depicting different tongue expressions along with the corresponding 2D renders.}
\label{fig:raw_dataset}
\end{center}
\end{figure}

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.95\linewidth]{exp_figs/point_cloud_comp.pdf}
\caption{Qualitative comparisons on various pointclouds from the test set of TongueDB. a) input image, b) ground-truth point-cloud, c) the point-cloud generated by TongueGAN and finally d) the point-cloud generated by point-cloud GAN \cite{li2018point}.}
\label{fig:point_cloud_comp}
\end{center}
\end{figure}

Quantitative results are provided in Table \ref{tab:errors} and qualitative results are presented in Fig.\ref{fig:point_cloud_comp}. For the quantitative results, we utilize the test set of our TongueDB and we measure the error based on the two commonly used type of distances when it comes to unordered 3D data, namely Chamfer Distance (CD) and Earth Mover's Distance (EMD) \cite{fan2017point}. As can be clearly seen in all of the comparisons, TongueGAN outperforms the compared methods by a large margin whereas the regressed tongue expression outperforms the rest of the methods.

\begin{figure*}[t]
\begin{center}
\includegraphics[width=1\linewidth]{exp_figs/qual_fig_new.pdf}
\caption{3D head reconstructions with tongue animations from ``in-the-wild'' images. From left to right, we depict the ``in-the-wild'' image, then the point-cloud generations from two viewpoints and finally the 3D head reconstruction with a zoomed-in area around the oral cavity.}
\label{fig:itw_qual}
\end{center}
\end{figure*}

\begin{table}[t]
\caption{Quantitative comparisons among the compared methods using CD and EMD as metrics. Lower values indicate better performance. TongueGAN achieves the best results in all settings.}
\label{tab:errors}
\vspace{0.3cm}
\centering
\begin{tabular}{|l|cc|}
\hline
\emph{Method} & \emph{EMD} & \emph{CD} \\
\hline\hline
\textbf{TongueGAN} & \textbf{1.62e-2} & \textbf{5.25e-5} \\
Tongue-Reg & 1.79e-2 & 1.10e-4\\
PC-GAN & 1.82e-2 & 1.13e-4 \\
TongueGAN\_v1  & 1.97e-2 & 1.67e-4 \\
TongueGAN\_v2 & 2.24e-2 & 2.09e-4 \\
\hline
\end{tabular}
\end{table}

\subsection{3D tongue reconstruction ``in-the-wild''}
\label{sec:ITW}
In this Section, we attempt to reconstruct the 3D surface of the tongue together with the entire head structure from ``in-the-wild'' images. In this set of experiments, we used all of TongueDB for training. We also added to our training data another $5K$ ``in-the-wild'' tongue images and created their 3D point-clouds using TongueGAN. Using all these data, we re-trained the pipeline according to Section \ref{sec:retraining}. The results are only visual as we do not have ground-truth point-cloud data to report quantitative comparisons.
Regarding the comparisons, we should note that PointCloud GAN \cite{li2018point} cannot be used in these experiments, as in order to work in the conditional setting, it needs as input the actual ground-truth point-cloud it attempts to reconstruct, something which we do not have at our disposal. Given that the TongueGAN variations (\textit{i}.\textit{e}., TongueGAN\_v1 and TongueGAN\_v2) perform worse than PointCloud GAN \cite{li2018point}, we only present results in this Section regarding TongueGAN.

Since our tongue regression method is based on the mean template mesh of UHM we can easily utilize the pipeline presented in \cite{ploumpis2020towards} in order to extent our approach to a particular facial identity. We begin by fitting a facial mesh to the image domain in order to get the 2D/3D landmarks and the identity of the subject and then we regress to the full head topology based on the UHM model. After reconstructing the head shape we crop the image around the projected 2D mouth landmarks. We then feed this cropped image to the re-trained pipeline and get the mean head shape with the tongue expression as mentioned in Section \ref{sec:tongue_recon}. Finally we merge the predicted tongue shape with the associated identity by treating the predicted tongue expression as a separate blend-shape.

Some 3D reconstructions can be seen in Fig. \ref{fig:itw_qual}. As evidenced, our pipeline is able to accurately reconstruct the 3D tongue details even in ``in-the-wild'' conditions. Additional tongue reconstructions of our method before and after the re-training framework against state-of-the-art methods can be seen in Fig. \ref{fig:qual_itw_test}. To further empirically validate that TongueGAN is able to capture the 3D structures of random tongues that are not included in the training set we provide linear interpolations between unseen latent features in the supplementary material.

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.95\linewidth]{exp_figs/method_comp_2.pdf}
\caption{Qualitative shape evaluation between our approach and the state-of-the-art methods of facial \cite{gecer2019ganfit} (GANFit) and head \cite{ploumpis2020towards} (UHM) reconstructions. We can easily deduce that the re-training framework plays an important role in the final tongue reconstruction from ``in-the-wild'' images.}
\label{fig:qual_itw_test}
\end{center}
\end{figure}

\section{Conclusion}
\label{sec:conclusions}
In this work, we presented the first pipeline which is able to perform 3D head and tongue reconstruction from a single image. To achieve this, we collected the first diverse tongue dataset with various tongue shapes and positions which we make publicly available to the research community. To also make this pipeline robust in ``in-the-wild'' images and to mitigate the absence of their corresponding ground-truth 3D tongue data, we introduced the first GAN method that is tailored for accurately reconstructing the 3D surface of a tongue from 2D images. As we show in a series of experiments, we are now able to accurately carry out 3D head reconstruction together with the tongue from a single image and thus create more realistic 3D avatars.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
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0-1 free by sea or from £49 by air. 2-11 from £55 by sea or from £119 by air, plus £25 per night, including breakfast in a bay or deluxe room only.


2nd Azerbaijan International Defence Industry Exhibition; Aydın Yazılım ve Elektronik Sanayii A.Ş. (AYESAŞ) and Azerbaijan National Academy of Sciences (ANAS) have started a cooperation for local and regional opportunities pursuant to the memorandum of understanding signed at ADEX 2016.
AYESAŞ and ANAS have agreed to lay foundations of the cooperation for potential projects, especially defence projects. Within this scope, foundations are laid to conduct mutual businesses in Azerbaijan and Turkey as well as third country markets pursuant to the Memorandum of Understanding signed in Baku on September 28, 2016. Large number of people from Azerbaijani local press and national defence press participated in the signing ceremony held in AYESAŞ stand. Researches initiated by the aforementioned agreement are planned to be realized together with the mutual works to include other institutes of National Academy of Sciences in order to be used in sub-systems of aviation and defence projects.


text:
Mario Aerts (Herentals, 31 de diciembre de 1974) es un exciclista profesional belga. Su victoria más importante la logró en el año 2002, cuando logró imponerse en la prestigiosa clásica Flecha Valona. En 2008 participó en los Juegos Olímpicos de Pekín obteniendo un octavo puesto en la carrera en ruta. Actualmente es director deportivo en el equipo Lotto Soudal.

Palmarés
1994
 1 etapa de la Vuelta a Valonia

1996
 Gran Premio de Isbergues

1997
 Circuito Franco-Belga

2001
 Giro de la Provincia de Lucca

2002
 Flecha Valona

Resultados en Grandes Vueltas y Campeonatos del Mundo

—: no participaAb.: abandono

Equipos
 Vlaanderen 2002-Eddie Merckx (1996-1997)
 Lotto (1998-2002)
 Lotto-Mobistar (1998-1999)
 Lotto-Adecco (2000-2002)
 T-Mobile (2003-2004)
 Team Telekom (2003)
 T-Mobile Team (2004)
 Lotto (2005-2011)
 Davitamon-Lotto (2005-2006)
 Predictor-Lotto (2007)
 Silence-Lotto (2008-2009)
 Omegha Pharma-Lotto (2010-2011)

Referencias

Enlaces externos
 Ficha y palmarés completo en sitiodeciclismo.net

Ciclistas de Bélgica en los Juegos Olímpicos de Pekín 2008
Directores deportivos
Ciclistas de Bélgica
Nacidos en Herentals

redpajama_set_name: RedPajamaWikipedia


Fankhauser H., Uske A., de Tribolet N.
The authors report 8 patients who developed an epidural hematoma after having been submitted to 1 or 2 computed tomographies (CT) which did not yet show the hematoma. The initial CT was done 1 to 6 hours after the accident. The delayed hematoma was diagnosed 3 and a half hours to 7 days after the accident. Secondary clinical deterioration occurred in 6 patients and was dramatic in 4 of them. Delayed epidural hematoma seems to be a frequent event; our 8 cases represent 10 % of all epidural hematomas operated during the period under consideration. This has to be taken into account when taking care of head injured patients after negative CT. The authors propose a list of indications for repeating CT. The current views on mechanisms of formation of epidural hematomas are discussed in the light of the increasing number of reported cases of delayed epidural hematoma.


Caramor is a Rustler 36 built in 1988. She is a very seaworthy boat designed in the late 60s by Holman and Pye. The Rustler 36 has a reputation for being well-built and very 'sea-kindly'. She also handles well and has a reasonable turn of speed for a heavy displacement boat.
Age and an element of neglect in the last few years before we aquired her mean't that we were able to buy her relatively cheaply but had a lot of work to do on her. In fact before the big trip we had three summers sailing her and four winters working on her. It would problably be quicker to list what we haven't renewed, rebuilt or changed than to list what we have done. The upside of this is that we know the boat inside out and are confident that every part of her meets the highest standards.
We had ample proof of her seaworthiness when we heard on the VHF 'force 10, imminent', when we were 30NM west of the Scillies. The crew had a little lip wobble but decided that the safest thing to do was to keep heading out to sea. So we spent the next few hours making all secure and changing down to storm sail and three reefs in the main. Caramor just took it all in her stride and although you don't go looking for that sort of thing the experience has done wonders for our confidence in the boat. We definitely chose the right one!
More information about Caramor and the work we have done on her will follow.


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text:
Book Launch: Charles Entrekin
Events, Launches
LEFT COAST WRITERS BOOK LAUNCH: Charles Entrekin, Author of Listening: New & Selected Work
Saturday, January 8, 2011 || 7pm
Book Passage-Corte Madera || 51 Tamal Vista Dr.
Corte Madera || www.bookpassage.com
Charles Entrekin was born in 1941 in Birmingham, Alabama,. He took his BA in English from Birmingham Southern College, in 1964. He left Birmingham in 1965 and lived in various states (New York , Tennessee, Alabama, and Montana) while pursuing advanced degrees in philosophy and creative writing. Arriving in California in 1969, he fell in love with the West Coast scene and the Hotel California experience. He now lives in Berkeley with his wife, poet, Gail Rudd Entrekin.
Charles has taught at almost every educational level. He taught pre-school language skills to six-year-olds with he Head Start program in Birmingham, Alabama; taught introduction to set theory to disadvantaged high school graduates with the Upward Bound Program in Tuscaloosa, Alabama; taught composition, English literature, creative writing, philosophy at the college level, and was the founder of the Creative Writing Program at John F. Kennedy University's Orinda, California campus.
For 24 years, Charles was the managing editor of The Berkeley Poets Cooperative and The Berkeley Poets Workshop & Press. The story of the Berkeley Poets Workshop & Press was written up as the cover story in the August 29, 1976 issue of the New York Times Magazine.
The Managing Editor of Hip Pocket Press (www.hippocketpress.com), Charles is also the author of In This Hour, a collection of poems, BPW&P, 1990; Casting For The Cutthroat & Other Poems, BPW&P, 1986; Casting For The Cutthroat, Thunder City Press, 1978, Birmingham, Alabama; All Pieces Of A Legacy, BPW&P, 1975, Berkeley, CA. His novel, Red Mountain, Birmingham, Alabama, 1965, was published May, 2008, by El Leon Literary Arts www.elleonliteraryarts.org .
We are celebrating his latest publication: Listening: New and Selected Work.
In "Listening: New and Selected Work," Charles Entrekin presents us with a remarkable poetic legacy. Written between 1975 and the present, the poems in this collection are passionate and darkly lyric. Always grounded in physical reality, they transcend time and place, revealing both the great and small moments of life as seen from the perspective of eternity.
—Mary Mackey
Book Launch, book passage, Charles Entrekin, LCW Book Launch, left coast writers, Library, literary, literature, poetry, poets, publishing


text:
Oregon är en delstat i nordvästra USA. Huvudstaden heter Salem; den folkrikaste staden är Portland. Oregon blev den 14 februari 1859 USA:s 33:e delstat. Invånarantalet i Oregon uppmättes till 3 421 399 år 2000, en ökning med 20,4 procent jämfört med 1990, och beräknades 2006 ha ökat till 3 700 758.

Historia
Innan européernas ankomst var området Oregon befolkat av flera indianstammar. De mest namnkunniga är Bannock, Modoc, Chinook, Klamath och Nez Perce. James Cook utforskade kusten 1778 på jakt efter Nordvästpassagen. Lewis och Clarks expedition reste genom regionen under sin utforskning av Louisianaköpet.
John Jacob Astor, finansiär från New York, grundade år 1811 Fort Astoria vid Columbiaflodens mynning för att utgöra en bas för hans pälshandelsföretag Pacific Fur Company och det var den första permanenta bosättningen för vita amerikaner i Oregon. Under 1812 års krig tog britterna kontrollen över pälshandeln och blev dominerande i nordvästra USA genom Hudson bay-kompaniet. Delstatens smeknamn är "Beaver State".

När pälsjägaren och entreprenören Ewing Young dog 1841 och inte hade några efterlevande som kunde ärva hans tillgångar och inget system fanns för att skifta ägarskapet av hans mark så samlades 1842 ett råd för att avgöra frågan. Detta var den första medborgarsamlingen innan Oregonlandet inträdde som delstat i USA.

Under 1842–1843 inleddes en stor inströmning av bosättare, mycket på grund av Oregonspåret genom Klippiga bergen och att amerikaner och britter kommit överens om att se Oregonlandet som gemensam mark. 1846 gjordes en överenskommelse i Oregonfördraget, som avgjorde USA:s rätt till området och 1848 formades Oregonterritoriet med nuvarande gräns mellan British Columbia och Washington som dess nordlig gräns.

Landöverlåtelseakten 1850, som gav alla ogifta vita män rätt till 160 tunnland och gifta par 320 tunnland mark, och den obligatoriska omflyttningen av ursprungsbefolkningen gjorde att många bosatte sig där. Den 14 februari 1859 trädde Oregon in i unionen som den 33:e delstaten.

Större städer
 Portland –  invånare (2005)
 Salem – 148 751
 Eugene – 144 515
 Gresham – 96 072
 Hillsboro – 88 300
 Beaverton – 85 775
 Medford – 76 850
 Bend – 75 290
 Springfield – 57 065
 Corvallis – 49 553
 Ashland – 21 443

Sport

Klubbar i professionella serier
 NBA – basket
 Portland Trail Blazers
 MLS – fotboll
 Portland Timbers
 Oregon State Beavers – amerikansk fotboll, fotboll(soccer/europeisk fotboll), handboll, baseball, basket
 Oregon Ducks – amerikansk fotboll, basket

Kända personer födda i Oregon
 Carl Barks, serietecknare
 Chris Botti, trumpetare
 Ty Burrell, skådespelare (Modern Family)
 Matt Groening, serietecknare, manusförfattare – Simpsons
 Summer Luv, porrskådespelare
 Linus Pauling, kemist, fredsaktivist, nobelpristagare
 River Phoenix, skådespelare
 Jane Powell, skådespelare, sångare
 Steve Prefontaine, lång- och medeldistanslöpare
 Tommy Thayer, gitarrist i det amerikanska hårdrocksbandet Kiss

Övrigt
 I Portland finns världens minsta officiella park – Mill Ends Park med en yta av 0,3 kvadratmeter.
 Oregon har ingen omsättningsskatt.
 Delstaten Oregon hävdar att den har världens kortaste flod, nämligen utloppet från Djävulssjön till Stilla Havet. Den kallas för D River, finns i sin helhet inom staden Lincolns gränser och är ca 36 meter lång (längden varierar något beroende på tidvattnet).
 I Oregon görs många Pinot Noir-viner
 Oregon är en av fem amerikanska delstater med kustlinje mot Stilla havet.
 Oregon var en av de amerikanska delstater som legaliserade marijuana under Jimmy Carter-administrationen i slutet av 1970-talet. Idag är marijuana enbart legalt för medicinskt bruk.

Se även
 Lista över countyn i Oregon

Källor

Externa länkar

 Oregons officiella hemsida

USA:s delstater


text:
Common sense committee launched
Competent Persons
The government has suggested that a new panel will help to put "common sense" back into health & safety. The Myth Busters Challenge panel chaired by the head of the HSE will be asked to look into complaints on advice given by non-regulators.
The committee would then look to provide guidance on whether the advice really was in keeping with what health & safety law requires. The panel has only been given an advisory role but hopes are that their findings will encourage bad decisions to be reversed.
The government has stressed that it sees "common sense" to be the key to successful health & safety. At MESH we wholeheartedly agree that health & safety needs to be based upon sensible decisions; and THAT over regulation really does need to be challenged.
All too often non-sensible decisions are made by people who fail to properly understand the reality of a situation; often because of a lack of experience or because they are afraid of being sued. The type of decisions the committee will review will in reality most likely come from those made by local councils; schools and other similar bodies.
It seems unlikely that the committee will judge on individual consultancy advice; which leaves employers in the same position that they will need to make careful checks before employing credible consultants.
We work throughout the UK and Europe delivering health and safety expertise for companies in a diverse range of industries.
Company directors convicted after major fires
Fire Service safety audits are declining
Employers urged to protect workers from welding fumes
A garden centre in Essex where a chemical spill left a worker with breathing difficulties has been fined £104k
DSEAR don't waste money by leaving the assessment until your operations are set up


Sevenoaks Rugby 1st XV secured an outstanding 13-7 win away at league-leaders Brighton Blues RFC on Saturday.
The win positions Oaks, who also defeated Brighton at the Paddock earlier this season, three points off the top slot, with Medway in third and Camberley in fourth breathing down their necks. London 1 South is set for what promises to be an exciting home stretch, the first of Oaks' final eight matches coming against Westcombe Park at the Paddock on Saturday 9 February following the upcoming rest week to mark the start of the Six Nations.
Oaks started the match in combative style, while Brighton, clearly still hurting from the loss at the Paddock, were pinged for over-exuberance at the breakdown enabling Oaks to strike the first blow in the form of a penalty from the opposition 10-yard line thanks to the trusty boot of Ben Adams.
Blues for their part began to move the ball convincingly up field and before long their huge pack had set up field position for the Blues 10 to jink his way through the Oaks defenders to dot the ball down for a converted try. With the tide turning and proceedings looking a little ominous for the visitors from Kent, the Blues yet again made an illegal infringement and the resulting Adams penalty pegged the deficit to 6-7 and Oaks were well in contention. Only 10 minutes had gone in this absorbing match and incredibly the score was to be unchanged for the next 65 minutes.
The match was characterised by the kind of niggle you expect to see when two teams at the top of their game slug it out, each trying hard to maintain their attacking shape and develop continuity in the face of quality and resolute defending. Something had to give, and so it was that Oaks, digging deep and falling back on all the hard work at the Paddock on a Tuesday and Thursday evening, found the edge.
With five minutes to go until the final whistle a series of mauls from Oaks saw the lads from the Paddock turn the screw, and rumble the ball towards the Blues whitewash for Matt McCrae to barrel over for a try, Adams adding the extras to make it 13-7.
Brighton threw everything they had at Oaks during the match's dying embers, and worked their way into the navy and gold 22 But Oaks stood strong as oak, and held firm to bag a hard-fought victory. Both sides to be fair played their parts in a titanic tussle.
Before the match the spectators had been treated to an entertaining curtain raiser in the shape of a friendly fixture between Sevenoaks Under 12s and Brighton Blues Under 12s, with honours even across a brace of matches, Oaks winning the first and Brighton the second.
The main match was particularly special for captain Stuart Coleman, securing his 150th Sevenoaks Rugby cap and credit must go to Coach Adam Bowman who following a training pitch misadventure, was pitch side to marshal proceedings on Saturday with the support of a pair of crutches. Thanks as ever to the vociferous travelling faithful, who were well and truly rewarded with a compelling spectacle in a win that continues Oaks' momentum on a memorable day when all four Senior Sevenoaks sides were in winning form; the 2s, 3s and the Gents winning convincingly against Beckenham, Canterbury and Ash respectively.