text:
Great societies need strong institutions
by Samantha Panchèvre
This article appears in the Digging Into Laudato Si' feature series. View the full series.
Social institutions | Society and Culture | MCAT | Khan Academy
Video of Social institutions | Society and Culture | MCAT | Khan Academy
(khanacademymedicine YouTube channel)
Chapter 4, sections 1 and 2
In chapter 4, Pope Francis introduces the concept of "integral ecology," or the interconnectedness of everything, combining sociology (the science of the structure and functioning of human society) and ecology (the science of how living things interact with their environment). Basically, it's his way of describing the necessity for an integrated approach to social and environmental justice. Francis writes: "Today, the analysis of environmental problems cannot be separated from the analysis of human, family, work-related and urban contexts, nor from how individuals relate to themselves, which leads in turn to how they relate to others and to the environment" (141).
Francis ties the "social" part of this ecology to the importance of institutions: "If everything is related, then the health of a society's institutions has consequences for the environment and the quality of human life. 'Every violation of solidarity and civic friendship harms the environment'. In this sense, social ecology is necessarily institutional… Within each social stratum, and between them, institutions develop to regulate human relationships. Anything which weakens those institutions has negative consequences, such as injustice, violence and loss of freedom" (142).
Institutions, such as those in the video above, are the structures that fulfill the needs of society. The most resilient societies have strong institutions built on public trust and collective participation. Unfortunately, Americans are facing a declining faith in institutions such as the federal government and the media. One recent observation from The New York Times contends this is because our institutions have lost the capacity to instill credibility within the people that represent them and have become platforms for performance instead.
Active participation at the community-level can build faith in institutions. Francis writes: "Attempts to resolve all problems through uniform regulations or technical interventions can lead to overlooking the complexities of local problems which demand the active participation of all members of the community" (144).
Thankfully, while a 2019 Pew Research Center report found that Americans believe public trust has been declining in the government and in each other, most think this decline can be turned around. Written responses to the Pew survey suggest more accountability, term limits, and restrictions on the role of money in politics could improve the level of confidence people have in the government. Meanwhile, confronting partisan tensions and overcoming tribal divisions at the community level, combined with a different approach to news reporting – one that emphasizes the ways people cooperate to solve problems – could improve peoples' confidence in each other.
Voting represents a tangible way individuals can strengthen their institutions, particularly at the local level. Find out about upcoming local elections in your state here.
Source material:
LS 4.1-2.pdf
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text:
Q: Выборка из БД названий таблиц В БД имеется несколько таблиц, их названия имеют такой формат:
dialog_login1_login2
dialog_login1_login3
dialog_login2_login3
dialog_login3_login1

Не совсем понимаю как мне составить sql запрос чтобы в результате он выдал например:
dialog_login1_login2
dialog_login1_login3

То есть выборка по фразе dialog_login1_*****
Либо такой результат:
dialog_login2_login3

То есть выборка по dialog_login2_*****
Надеюсь понятно объяснил.

A: Должно помочь
SELECT *
FROM INFORMATION_SCHEMA.TABLES
WHERE TABLE_NAME LIKE  'dialog_login1_%'

redpajama_set_name: RedPajamaStackExchange


text:
<?php

namespace OC\AppFramework\Http;

use \OC\AppFramework\Middleware\MiddlewareDispatcher;
use \OC\AppFramework\Http;
use \OC\AppFramework\Utility\ControllerMethodReflector;

use OCP\AppFramework\Controller;
use OCP\AppFramework\Http\Response;
use OCP\AppFramework\Http\DataResponse;
use OCP\IRequest;

/**
 * Class to dispatch the request to the middleware dispatcher
 */
class Dispatcher {
	private $middlewareDispatcher;
	private $protocol;
	private $reflector;
	private $request;

	/**
	 * @param Http $protocol the http protocol with contains all status headers
	 * @param MiddlewareDispatcher $middlewareDispatcher the dispatcher which
	 * runs the middleware
	 * @param ControllerMethodReflector $reflector the reflector that is used to inject
	 * the arguments for the controller
	 * @param IRequest $request the incoming request
	 */
	public function __construct(Http $protocol,
								MiddlewareDispatcher $middlewareDispatcher,
								ControllerMethodReflector $reflector,
								IRequest $request) {
		$this->protocol = $protocol;
		$this->middlewareDispatcher = $middlewareDispatcher;
		$this->reflector = $reflector;
		$this->request = $request;
	}

	/**
	 * Handles a request and calls the dispatcher on the controller
	 * @param Controller $controller the controller which will be called
	 * @param string $methodName the method name which will be called on
	 * the controller
	 * @return array $array[0] contains a string with the http main header,
	 * $array[1] contains headers in the form: $key => value, $array[2] contains
	 * the response output
	 * @throws \Exception
	 */
	public function dispatch(Controller $controller, $methodName) {
		$out = [null, [], null];

		try {
			// prefill reflector with everything thats needed for the
			// middlewares
			$this->reflector->reflect($controller, $methodName);

			$this->middlewareDispatcher->beforeController($controller,
				$methodName);
			$response = $this->executeController($controller, $methodName);

			// if an exception appears, the middleware checks if it can handle the
			// exception and creates a response. If no response is created, it is
			// assumed that theres no middleware who can handle it and the error is
			// thrown again
		} catch (\Exception $exception) {
			$response = $this->middlewareDispatcher->afterException(
				$controller, $methodName, $exception);
			if ($response === null) {
				throw $exception;
			}
		}

		$response = $this->middlewareDispatcher->afterController(
			$controller, $methodName, $response);

		// depending on the cache object the headers need to be changed
		$out[0] = $this->protocol->getStatusHeader($response->getStatus(),
			$response->getLastModified(), $response->getETag());
		$out[1] = \array_merge($response->getHeaders());
		$out[2] = $response->getCookies();
		$out[3] = $this->middlewareDispatcher->beforeOutput(
			$controller, $methodName, $response->render()
		);
		$out[4] = $response;

		return $out;
	}

	/**
	 * Uses the reflected parameters, types and request parameters to execute
	 * the controller
	 * @param Controller $controller the controller to be executed
	 * @param string $methodName the method on the controller that should be executed
	 * @return Response
	 */
	private function executeController($controller, $methodName) {
		$arguments = [];

		// valid types that will be casted
		$types = ['int', 'integer', 'bool', 'boolean', 'float'];

		foreach ($this->reflector->getParameters() as $param => $default) {

			// try to get the parameter from the request object and cast
			// it to the type annotated in the @param annotation
			$value = $this->request->getParam($param, $default);
			$type = $this->reflector->getType($param);

			// if this is submitted using GET or a POST form, 'false' should be
			// converted to false
			if (($type === 'bool' || $type === 'boolean') &&
				$value === 'false' &&
				(
					$this->request->method === 'GET' ||
					\strpos($this->request->getHeader('Content-Type'),
						'application/x-www-form-urlencoded') !== false
				)
			) {
				$value = false;
			} elseif ($value !== null && \in_array($type, $types)) {
				\settype($value, $type);
			}

			$arguments[] = $value;
		}

		$response = \call_user_func_array([$controller, $methodName], $arguments);

		// format response
		if ($response instanceof DataResponse || !($response instanceof Response)) {

			// get format from the url format or request format parameter
			$format = $this->request->getParam('format');

			// if none is given try the first Accept header
			if ($format === null) {
				$headers = $this->request->getHeader('Accept');
				$format = $controller->getResponderByHTTPHeader($headers);
			}

			$response = $controller->buildResponse($response, $format);
		}

		return $response;
	}
}

redpajama_set_name: RedPajamaGithub


text:
#WalangPasok – August 10 2021 declared holiday in selected Mindanao provinces for Islamic New Year
Tsunami warning raised across east Philippines after magnitude-8.2 quake in Alaska
#WalangPasok – August 19 2021 declared holiday in Quezon City
#WalangPasok / history / holiday announcements
PD July 28, 2021 ; MD August 19, 2021 by Mark Pere Madrona
Tagged august 19 2021 holiday philippines, august 19 2021 holiday proclamation, august 19 2021 holiday quezon cityLeave a Comment on #WalangPasok – August 19 2021 declared holiday in Quezon City
The Philippines will be making the 143rd birth anniversary of President Manuel L. Quezon this August 19, Thursday. The day is a special non-working holiday in Quezon City as well as the provinces of Aurora and Quezon as mandated by Republic Act 6741. It is a working holiday in the rest of the Philippines. The law was enacted during the term of the late President Corazon Aquino. Read the full text of RA 6741 in this link.
Quezon is the country's second president, having served from 1935 until his death in 1944. He is also the third longest-serving president in Philippine history, behind Ferdinand Marcos and Gloria Macapagal-Arroyo.
He was the first of three presidents that led the country during the Commonwealth years. As frequently mentioned in history books, it is a ten-year transition period beginning in 1935 before the United States granted the Philippines independence. That period of guided self-governance was interrupted by Japanese occupation during World War II.
August 19, 2021 is a special non-working day in Quezon City
Quezon City was officially created in 1939 through Commonwealth Act #502, which was passed during the term of President Manuel Quezon. By 1948, Quezon City was proclaimed the capital city and the seat of the national government through Republic Act 333 which was signed into law by then President Elpidio Quirino. It remained that way until 1976, when Ferdinand Marcos signed Presidential Decree 940 which made Manila once again the capital city of the country.
According to the 2020 DOLE handbook on workers statutory monetary benefits, the "no work, no pay" principle applies during special non-working holidays. Meaning, workers who are not required or permitted to work on special non-working days are not entitled to any compensation unless there is a collective bargaining agreement or a company policy that provides for that.
The handbook likewise highlighted that employees who work during special non-working days are entitled to receive an additional pay of 30% for every hour that they render service.
Please like the Facebook page of "The Filipino Scribe"!
#WalangPasok - August 10 2021 declared holiday in selected Mindanao provinces for Islamic New Year

redpajama_set_name: RedPajamaCommonCrawl


Don't miss the latest happenings out of Engadin St. Moritz, including new lifts, terrain expansions, weather, photo galleries, infographics and current events skiers and mountain travelers should know about. Stay up-to-date on Engadin St. Moritz news and events by clicking on articles from the list below or page through to see coverage from the entire ski season.
St. Moritz wins "Best European Ski Resort" ahead of Val d'Isere and Chamonix.
Gstaad's multi-million-euro investment plan is on target; the resort opened a new gondola over Christmas.
Heavy snow over the weekend has fallen in Switzerland, as low as 1200 metres, closing five mountain passes since Saturday.
Be one of the first to hit the slopes this year: the Diavolezza ski area (2195-2978m) in Engadin St. Moritz has opened for the winter season in Oct.


Kaduna State Commissioner of Police, Umar Shehu, yesterday paraded Bulus Kinze and James Kinze, both from Kogun village of Jema'a Local Government Area of Kaduna State for allegedly using their residence for illegal manufacturing of arms and ammunitions and equally serving as main supplier of arms to armed bandits within the area.
According to the police commissioner, when the suspects were arrested and their residence, two locally made AK-47 rifles, one locally made SMG rifle and its magazine, two locally made pistols, two police coloured AK-47 magazines, one locally made AK-47 magazine, twenty live 0.36 special revolver ammunition, three live 7.62mm long ammunitions, eleven live 7.62mm short, AK-47 ammunition and three live cartridges.
Others are one live 5.5mm, T.6 ammunition, 14 empty shells of 7.62mm short, AK-47 ammunition, seventeen empty shells of 0.36 special revolver pistols ammunition, one expanded cartridges, some parts of locally made AK-47 rifle and parts of other rifles, one drilling machine use in production of rifles, one hand driller, some instrument for fabrication of rifles and some assorted charms.
Meanwhile, when one of the suspected manufacturers of the illegal arms, James Kinze was interviewed at the police command headquarters where he was paraded alongside other armed robbery suspects, he owned up to manufacturing of locally made arms but said it was meant to protect themselves against bandits that have continued to terrorise their areas unabated.
"Armed bandits have continued to terrorise our area and killing our people, so since we could not get adequate protection from the security agencies, we decided to produce locally made arms to protect ourselves and our people from the recurring attacks by such arm bandits," he said.
Also paraded were 20 suspected persons who were arrested for various offences ranging from, armed robbery, criminal conspiracy, and various acts of terrorism.
Meanwhile, CP Umar has reassured members of the public of the readiness of the command to rid the state of all criminal elements in the society.
He urged residents of Kaduna State to continue assisting the command with useful information to track down the criminals from every nook and crannies of the state.
The Commissioner informed that as soon as investigation into the various offences is concluded, the suspects would be charged to court for prosecution.


text:
Johnson v. Perez, Civil Action No. 11–cv–1832 KBJ
Writing for the Court KETANJI BROWN JACKSON, United States District Judge
Citation 66 F.Supp.3d 30
Parties Prince Johnson, Plaintiff, v. Thomas E. Perez, Secretary, U.S. Department of Labor, Defendant.
Docket Number Civil Action No. 11–cv–1832 KBJ
Decision Date 02 September 2014
66 F.Supp.3d 30
Prince Johnson, Plaintiff
Thomas E. Perez, Secretary, U.S. Department of Labor, Defendant.
Civil Action No. 11–cv–1832 KBJ
Signed September 2, 2014
Alan Lescht, Constance Travanty, Rani V. Rolston, Alan Lescht & Associates, PC, Washington, DC, for Plaintiff.
Mercedeh Momeni, U.S. Attorney's Office, Washington, DC, for Defendant.
Plaintiff Prince Johnson is an African–American man who was employed for approximately seven months—from April 2, 2006, until November 11, 2006—as a Veterans Employment Specialist in the Veterans Employment and Training Services ("VETS") division of the Department of Labor ("DOL" or "Defendant") before his
employment was terminated.1 Johnson has brought the instant action against DOL, alleging two counts of employment discrimination in violation of Title VII of the Civil Rights Act of 1964, 42 U.S.C. §§ 2000e —2000e–17. In the first count of his complaint, Johnson alleges that his termination was due to his race. In the second count, Johnson alleges that he was subjected to a hostile work environment on account of his race during the course of his employment.
Before this Court at present is Defendant's Motion for Summary Judgment on both counts. (Def.'s Mot. for Summ. J., ECF No. 14 at 1–2.) In that motion, Defendant maintains that the undisputed record evidence demonstrates that Johnson was terminated for legitimate, non-discriminatory reasons, and that Johnson has failed to produce sufficient evidence to establish that he was subjected to a hostile work environment. Because this Court concludes that there is no genuine issue of material fact as to whether Johnson experienced race discrimination or was subjected to a hostile work environment, and that Defendant is entitled to judgment as a matter of law, it will GRANT Defendant's motion as to both of Johnson's claims. A separate order consistent with this Opinion will follow.
A. Factual Background
Johnson's tenure at VETS was brief, and the undisputed material facts underlying his lawsuit are equally so. Johnson was initially hired as a Veteran's Employment Specialist within VETS in April of 2006. (Suppl. Decl. of Complainant Prince Johnson ("Pl.'s Suppl. Decl."), Ex. 1 to Pl.'s Opp'n to Def.'s Mot. for Summ. J., ECF No. 16–1 at 2–11, ¶¶ 4, 6; see also Def.'s Statement of Undisputed Material Facts ("D–SOF"), ECF No. 14 at 3–9, ¶ 6.)2 Johnson's initial appointment was a temporary Veterans Readjustment Appointment ("VRA") scheduled to last for 60 days with the possibility of an extension or conversion to a career conditional appointment. (See Pl.'s Suppl. Decl. ¶ 6; see also D–SOF ¶ 6.)
Gordon Burke, an African–American man who was VETS's Director of Operations at the time, was the agency official responsible for hiring Johnson. (D–SOF ¶ 2.) Prior to his hiring, Johnson interviewed with both Burke and Pamela Langley, a Caucasian female who served as Johnson's first-line supervisor during his employment with VETS. (Id. ¶ 4.) At the interview, Langley informed Johnson that his primary responsibility would be to assist Langley's subordinate, Patrick Hecker, with developing and maintaining Excel spreadsheets related to VETS's "Jobs for Veterans" program. (Id. ¶ 5.) After the interview, Langley told Burke that, based on Johnson's resume, she believed that Johnson had enough experience to be able to perform the tasks required for the position successfully. (Id. ¶ 4.) Shortly thereafter, Burke hired Johnson. (Id. ¶ 6.)
Johnson's relationship with his new supervisors got off to a rocky start. A few weeks into Johnson's tenure, Hecker, who is a Caucasian man, reported to Langley
that Johnson was having difficulty completing his assignments on time and without errors, and that Johnson had reacted defensively when Hecker tried to discuss these performance issues with him. (Id. ¶ 7.) Langley then raised concerns about Johnson's work in a meeting with Burke, and recommended that Johnson receive Excel and other general training. (Id. ¶ 9.) In May of 2006, Johnson also met with Burke and told Burke that Johnson was not getting appropriate support from Hecker and Langley and, consequently, felt like he was being "set up to fail." (Id. ¶ 10; see Pl.'s Suppl. Decl. ¶ 65.) Burke then convened a meeting with Johnson, Hecker, and Langley to discuss reported issues with Johnson's performance. (D–SOF ¶ 11; Pl.'s Suppl. Decl. ¶ 31.) At the meeting, Burke directed Johnson to undertake the training activities Langley recommended, including a basic-level course in Excel, and Johnson eventually did so. (D–SOF ¶¶ 11, 15; Pl.'s Suppl. Decl. ¶¶ 31–32.)
Notwithstanding Johnson's additional training, his professional relationship with Hecker and Langley continued to deteriorate. At one point, Langley returned from leave to find that on June 30, 2006, in Langley's absence, Burke had authorized the conversion of Johnson's temporary appointment to a career conditional appointment when Burke signed paperwork that he believed was urgently necessary for Johnson to receive his next paycheck. (D–SOF ¶¶ 16–18; see also Pl.'s Suppl. Decl. ¶ 7.) Langley subsequently spoke to Burke and recommended that Johnson's appointment remain temporary because of ongoing performance issues. (D–SOF ¶ 18.) Burke then contacted the human resource office and requested that the office cancel Johnson's conversion to career conditional status. (Id. ¶ 19.)
Although Hecker, Langley, and Johnson met several times between July and October of 2006 to discuss Johnson's performance and his "argumentative demeanor[,]" (Id. ¶ 22; see Pl.'s Suppl. Decl. ¶ 61), these sessions apparently did not mitigate the problems, and on October 6, 2006, Langley informed Johnson that she would be recommending to Burke that Johnson be terminated. (D–SOF ¶ 23; Pl.'s Suppl. Decl. ¶ 33.) On October 10, 2006, Johnson received a letter from Burke notifying him of Burke's decision to terminate Johnson's employment, effective November 11, 2006. (D–SOF ¶ 25.)
B. Procedural History
After exhausting his administrative remedies, Johnson filed the instant complaint in this Court on October 17, 2011, alleging that Defendant violated Title VII with respect to both Johnson's termination and to the conditions of Johnson's employment. (Compl., ECF No. 1, ¶¶ 74–75, 77.)3 The complaint alleges that Johnson's termination was the result of discrimination based on his race (Count I), and also that Johnson was subjected to a hostile work environment because of his race while employed at VETS (Count II). (Id. )
In the instant motion for summary judgment, which was filed on December 11, 2012, Defendant contends that judgment should be granted in its favor on both counts. Defendant argues that Count I cannot withstand summary judgment because DOL has articulated legitimate, nondiscriminatory
explanations for Johnson's termination—namely Johnson's poor work performance and argumentative demeanor—and Johnson has failed to show that this explanation was merely pretext for race discrimination. Count II must also fall, Defendant claims, because the record evidence is insufficient to support Johnson's claim that he faced a hostile work environment while employed at VETS. In response, Johnson asserts that he has identified evidence sufficient to create a triable issue of fact regarding whether Defendant's legitimate, nondiscriminatory explanations were a pretext for race discrimination, and that the record evidence shows that Johnson experienced working conditions that were sufficiently severe and pervasive to constitute a hostile work environment.
This Court has reviewed the entirety of the record, and has concluded that Johnson has failed to raise a question of material fact as to whether DOL's proffered reasons for Johnson's termination were a pretext for race discrimination. Johnson offers no evidence that racial animus was at the root of the allegedly discriminatory treatment, and especially in light of the fact that Johnson was hired and fired by the same persons—one of whom was African American—Johnson has failed to produce facts from which a reasonable jury could infer that his superiors' actions were because of race. The Court further concludes that Johnson has failed to support his hostile work environment claim because the acts on which Johnson bases his claim are not sufficiently severe and pervasive to meet the high standard for such claims. Thus, as explained further below, this Court will enter summary judgment in favor of Defendant on both counts.
II. LEGAL STANDARD
The Court must grant summary judgment if the moving party demonstrates that there is no genuine dispute as to any material fact and that the moving party is entitled to judgment as a matter of law. Fed.R.Civ.P. 56(a). "A fact is material if it 'might affect the outcome of the suit under the governing law,' and a dispute about a...
Benton v. Laborers' Joint Training Fund, Civil Action No.: 14–1073 (RC)
United States District Courts. United States District Court (Columbia)
...misconduct" does not contradict the Fund's proffered non-retaliatory explanation for Ms. Benton's termination. Cf. Johnson v. Perez, 66 F.Supp.3d 30, 39 (D.D.C.2014) (holding that there was "nothing contradictory" about statements that employee was fired: (1) to "support [the employee's] su......
Townsend v. United States, Civil Action No. 15–1644(BAH)
...are certainly far more hostile and blatantly discriminatory than the conduct at issue in this case. See also Johnson v. Perez , 66 F.Supp.3d 30, 44 (D.D.C. 2014), aff'd , No. 15-5034, 2015 WL 5210265 (D.C. Cir. July 1, 2015) (rejecting a hostile work environment claim on summary judgment wh......
Walden v. Patient-Centered Outcomes Research Inst., Civil Action No. 15–1034 (TJK)
U.S. District Court — District of Columbia
...is not sufficient to overcome summary judgment." Yue Yu v. McGrath , 597 Fed.Appx. 62, 67 (3d Cir. 2014) ; see also Johnson v. Perez , 66 F.Supp.3d 30, 42 (D.D.C. 2014) (finding no evidence of pretext where employer was purportedly "tense and uncomfortable" around plaintiff, who was black, ......
Warren v. Pon, Civil Action No. 14–1094 (BAH)
...raised voices and criticism may have been, these circumstances do not create a hostile work environment. See, e.g., Johnson v. Perez , 66 F.Supp.3d 30, 44 (D.D.C. 2014) ("[E]ven when viewing the facts in the light most favorable to Johnson and taking as true his experiences of being 'yelled......
Judge Ketanji Brown Jackson Is Confirmed As Next SCOTUS Justice
...for plaintiffs seeking to establish employer wrongdoing and causation in employment discrimination cases. See, e.g., Johnson v. Perez, 66 F.Supp.3d 30 (D.D.C. 2014) (granting summary judgment where weight of evidence did not suggest adverse employment action was pretext for unlawful discrim......


Founded in 1981, China Acrobats Association (CAA) is a non-governmental organization of professional acrobats from all ethnic groups all over China under the guidance of CPC. Serving as the bridge and bond between CPC and government as well as acrobatic circle, CAA is considered to be a key element in prospering Socialist literary and art cause and building up a Socialist cultural power. CAA is a group member of CFLAC.
CAA implements the literary and art guideline policies by CPC and adheres to the people's orientation in work and creation. While catering for the people at all levels,CAA strides with the tide in conducting innovative reforms and making functions in consolidation, guidance, communication, coordination, service and management, self-discipline and rights protection among acrobats. By organizing training, inspiration seeking deep into life, awards and critics, showcase of creation, theoretic exchange, publication, art voluntary service, lawful rights protection and exchange with foreign counterparts, CAA is devoted to the prosperity of Chinese acrobatic cause as a sweet and cozy home for all Chinese acrobats.
CAA currently practices group membership and individual membership simultaneously. Currently CAA consists of 30 group members, namely acrobats associations in the 20 provinces, 4 autonomous regions and 4 municipalities, as well as Acrobats Association of Xinjiang Production and Construction Corps. Besides, CAA is blessed with over 2900 individual members at the state level. The supreme authority of CAA is the National Congress that elects into its board council. The president and several vice presidents are elected into the Presidium that appoints one secretary-general and several deputy secretaries-general. The National Congress of CAA is convened every five years and during its recess the board council implements the approved resolutions by National Congress. During the recess of the board council, the Presidium takes charge of resolutions by National Congress as well as the board council and executive vice president is in charge of daily work of CAA. The headquarters of CAA is located in Beijing.


At the beginning of the Spanish Civil War the republican authorities started organising what came to be known as "children's colonies". These children's colonies became both home and school for all the children who were evacuated from Madrid. The purpose of this article is to study in depth the transformation of many of these children's colonies into educational communities. The teachers accompanying the children often shared the republican ideals of "active citizenship" and strove to make conscious citizens out of these children. The educational model chosen for achieving this aim was the community model, based on the transformation of every children's colony into a self-sufficient community of teachers and students, along the lines of similar experiences organised by the international New Education movement in the 1920s and '30s. The article discusses the iconic experiences of children's colonies during the Spanish Civil War and the ways in which their different concepts of "community" were represented. By studying the photographic collection put together from different public and private archives alongside the written information preserved in unpublished diaries we are able to develop a methodological model for analysing the construction and/or destruction of the community ideal in these educational experiences.


text:
Get In Touch With Our London Office
Mon - Fri 9.00 - 18.00 GMT
+44 (0)208 212 7328 USA: +1 213 223 7202
Visas for Temporary Workers (Creative and Sporting)
Category: Immigration
*This route has now been replaced with Temporary Work – Creative Worker Route
At ENT imm, we go to great lengths to help our clients gain access into the UK, being highly knowledgeable on which routes of entry are required.
If you are coming to Britain for paid, temporary work for up to a year, you will require a visa application under the Tier 5 Creative and Sporting category, which clearly identifies the type of work you will be carrying out and the duration for which you will be residing in the country.
You can apply for a Tier 5 Temporary Worker – Creative and Sporting visa if:
You have been offered work as a sports person or creative worker in the UK
You are from outside the European Economic Area (EEA) and Switzerland
You meet the eligibility requirements
Trusted by Music industry professionals to deliver. We're ready to assist you!
Tier 5 Sponsorship Explained
Who is a Creative Worker?
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Select the cutting tool and determine the amount of cutting is very important in CNC machining process, it is not only related to the machining efficiency, but also directly affect the quality of the workpiece processing. Here I'd like to introduce how to choose CNC machining cutting tools, hope it is useful for you.
The choice of the tool should be based on the performance of the workpiece material, machine tool processing capacity, cutting dosage, processing procedures and other related factors.
The general principle of tool selection is: easy installation and adjustment, durability and precision, good rigidity. In the premise of meeting the processing requirements, try to choose a shorter tool handle to improve the rigidity of processing. When selecting the tools, make the machined workpiece surface size adapt to the tools size and tool size.
Often use the end mill to machining the outline around the planar parts planar parts.
Use cemented carbide cutter milling plane.
Use high-speed steel milling cutter processing boss, groove.
Use corn milling cutter with carbide inserts machining rough surfaces or roughing holes.
Often use ball-end milling cutter, ring cutter, cone cutter and disc cutter processing three-dimensional surface and variable profile contour.
In the free-form surface machining, the tip of the ball cutter cutting speed is zero, therefore, in order to ensure machining accuracy, cutting row spacing is generally made very dense, so the ball is commonly used in finishing machining the curved surface.
Flat-end cutter is better than ball-end cutter in the surface processing quality and cutting efficiency, so long as the guarantee is not cut under the premise, whether roughing or finishing machining curve surface, should be given priority to the flat-end cutter.
In the machining center, a variety of tools were mounted on the cutter magazine, according to the procedures choose cutters at any time. Therefore, the standard shank must be used in order to make the standard tools used for drilling, boring, expanding and milling processes quickly and accurately installed on the machine tool spindle or magazine. Should be to minimize the number of tools; a tool after clamping should be complete all the area where it can machining; rough machining and finishing machining of the tools should be used separately even if the same size of the tools; milling first then drilling; finish machining curve surface first and then For two-dimensional contour finishing machining; Where possible, should be used as far as possible CNC automatic tool change function to improve production efficiency.


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Among serious Mac users, the Touch Bar seems to be one of the more controversial moves Apple has made in recent years. The most common critique I've seen is that it simply lacks utility; that there still aren't enough apps which provide compelling functionality for it. Harsher criticisms have called for it to be gotten rid of altogether, or at least for a high-end MacBook Pro which retains the row of function keys that it replaced. I think there's a lot that could be done to improve the Touch Bar for its hypothetical second generation, though, at least in terms of the hardware complaints.
I don't own a Touch Bar MacBook Pro myself, though my brother does so I've used one quite a bit. I like it, but I don't feel like I'm missing out on that much with my non-Touch Bar 2014 model, and there's a distinctly unpleasant lack of tactility when using the Touch Bar. I'd sum it up by saying it just doesn't feel like a part of the keyboard, which it seemingly aims to. Perhaps the main reason for this is that you can't really touch type on it (no pun intended...), you have to look where you're placing your finger, because actions trigger as soon as it makes contact.
So how could it be improved? Well, I think Apple already has precisely the tools for the job: 3D Touch and the Taptic Engine.
The idea is simple: the Touch Bar should do nothing when just touched, and only trigger a function when the user presses harder.1 There could be some nice haptic feedback to match: a click like the iPhone 7/8 home button, or the Force Touch Trackpad. Running a finger along the '3D Touch Bar' could also trigger little haptic taps as you cross the threshold from one button into another,2 and that would go at least some way towards approximating the tactility of a real keyboard. It might even, if it were good enough, allow navigation by touch alone. For more dynamic content, like sliders, it could obviously fall back to touch-and-drag behaviours, and haptic feedback would augment those interactions just like on iPhones. Since pressure sensitivity would be there, a more vigourous tap on the 3D Touch Bar (dare I say, like tapping a key on a physical keyboard) could trigger a button immediately, and skip the potentially slower touch-then-press method I'm getting at above.
A second-gen Touch Bar like this seems relatively easy to implement; it'd require only the addition of technology Apple is already producing in enormous quantity for the iPhone. Sure, it would be a change from the current interaction model, but when considering the huge changes watchOS has undergone in its first couple of years, and the relatively few Mac models which include a Touch Bar, I don't see that being a stopping block. Of course, there seem to be plenty of people for whom this kind of Touch Bar would be as bad as the current one, who simply want a high-end version of the MacBook Pro with function keys. Conversely, enough people may like the Touch Bar as-is, so maybe the incentive to change it just isn't there.
Finally, it's important to note that this doesn't directly address the other complaint I referenced in the introduction: the lack of compelling functionality. I think the core value proposition of the Touch Bar - dynamic keyboard buttons - is sound, and compelling functionality is more likely to appear if the underlying hardware is equally compelling.


Tony Burton, who starred as Apollo Creed's trainer Duke in the Rocky franchise, has died.
Creed helmer and co-writer Ryan Coogler has been appointed as the first Warner Bros Creative Talent Ambassador. The studio's program supports the next generation of UK talent, with scholarships that provide work placements, mentoring, apprenticeships and training courses.
Can iconic film characters like Rocky Balboa ever die onscreen? Certain studio executives would answer "no" — claiming characters such as James Bond and Indiana Jones will live on forever. After all they're more than characters, they're a franchise business. Why kill them?
Awards season is looking good for Sylvester Stallone, who just won the Golden Globe for supporting actor in film for his turn as an aged, retired and ill Rocky Balboa in Ryan Coogler's Creed.


Do you want to explode your marketing and sales projects following How To Use Domain With Clickfunnels If I Already Have Shopify? You need to be looking at creating sales funnels. A lot of individuals do unknown where or the best ways to begin. ClickFunnels demystifies and streamlines the process of developing sales funnels so you can take your business to brand-new heights. Exactly what is Clickfunnels and is it worth the hype?
When your prospects engage with you, you have to direct them through your sales process. A marketing funnel takes your potential customers from that preliminary engagement through actions that are planned to result in a conversion. A conversation does not always need to suggest purchasing. Conversions can also be downloading documents, signing up for a newsletter or taking part in a webinar. A funnel usually has different conversions at various points. Funnels are various. They can be easy or extremely complex, but they all need to lead to something – increasing conversions and sales.
Exactly what is How To Use Domain With Clickfunnels If I Already Have Shopify?
ClickFunnels is a tool created to help develop and execute sales funnels. You might need numerous software and online tools to create some of these which can be a lot. With ClickFunnels you can do all that and more on one platform.
Creating a website takes some time that would be much better spent on dealing with your sales procedure. If you currently have a website that's great, but with ClickFunnels, a site is not the end-all and be-all of marketing. You do not even need a website to start using ClickFunnels!
Starting is simple. ClickFunnels uses a 14-day trial that let's you check out the product prior to you buy it. This is terrific as it offers you an opportunity to see how it works and explore how you can utilize it for your very own service.
There are training tutorials that you can see and ClickFunnels gets you to watch these videos by using a totally free T-shirt (and getting you to choose in on the subscriber list). The training videos are short but they discuss how whatever works really plainly.
There is variety pre-built sales funnels that you can select from that match your service. You begin by choosing your sales funnel. You can produce and grow your subscriber list with the Lead Capture funnel, sell product or services with the Sales Page Funnels, promote webinars with Webinar Funnels and more. After selecting your funnel you can then pick a template. ClickFunnels has different design templates that you can pick from. These have all been tested and proven to work so you can survive your A/B testing phase quickly.
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Nevertheless a funnel isn't almost developing pages, however also about getting them to link to particular action circulations and integrating things like autoresponders and payment processors for a more automatic procedure. ClickFunnels offers that one platform to develop your whole funnel. You also get tracking and analytics to assist you see the efficiency of each funnel so you can optimize and improve it.
ClickFunnels is an exceptional, simple item to utilize for structure sales and marketing funnels. The most important element of having the product is the education you will receive from it. There is a lot to learn about funnels and this product unloads everything in an easy to understand way. It may not be the most affordable product on the market however thinking about that it gets rid of all the additional items you typically would have needed, it is really cost effective and worth it.


I thought it was worth updating my thoughts on this camera after 3 months of reasonably heavy use, as a few things have changed with the system and how I use it. There is also new competition on the block with the upcoming Nikon D850 so I'll add in my thoughts on that ( as someone who suffers from severe Gear Aquisition Syndrome the D850 and to a lesser extent the Sony A9 have given me serious pause for thought and I know I'm not the only one ).
So how have things changed? There have not really been any groundbreaking firmware updates from Fuji themselves, but a few things have happened to change how I use the system. First off, Godox has released a new trigger ( X1T-F ) for Fuji cameras that allows you to use high speed sync with their latest flashes, including my favourite, the AD200. This has meant that for me personally, the flash sync speed of 1/125 is no longer an issue. I've not used HSS that much, and if I do, its generally only to keep the shutter speed a little higher on the 32-64 so that I don't need a tripod. Its awesome anyway, and I'm still exploring what I can do with this system ( I have 3 godox flashes now ).
The next thing is the rapidly changing adapted lens scene. This has really taken off on this camera and gives you a lot of creative options which I love. This is a shot of my daughter taken with the Minolta 45mm f2, which is one of a number of great, cheap options you can use with your choice of adapter.
It's not easy nailing the shot with this lens, I tend to just click to zoom in and try and focus that way. Using the peaking aids doesn't really work with these lenses. If you want to see someone who has really mastered these lenses check out Jonas Rask. Here are another couple of shots taken with the Minolta. They are not super sharp like the Fuji glass but I love the look they give with this awesome sensor.
And it gets better..Steelspring and Techart are both bringing out smart adapters for Canon glass. These will provide accurate, if slightly slow AF on all Canon lenses. Imagine a 135 f2 on this camera with accurate autofocus...That is when you will truly see the medium format look for full body shots ( it will probably be too thin a depth of field for portraits, but fun to focus on that eyeball and watch everything else vanish into glorious bokeh balls ). Kipon also have something in the works in this area and are apparently working with Fuji on some special features, so the smart adapter scene is looking very exciting.
1. I enjoy using it. That is the number 1, most important thing about this camera. I've grown to love the Fuji ergonomics and shooting experience and the GFX is just an extension of that. Moving from my xpro-2 or x100F to this camera is very easy. All modern cameras are amazing, the D850 smokes the GFX on specs but I know for a fact I would not enjoy using it, it would just be a tool to get the job done.
2.The Fuji lenses - I have used a lot of Nikon and Canon glass ( G.A.S = system switcher ). None of it comes close to the Fuji GFX lenses for me. You buy into a system for the glass, not the camera body and the Fuji glass is stellar.
3. The EVF. I'm so used to using an EVF, I'm not sure I can go back to the DSLR world.
So thats the D850, what about the Sony A9...This camera has seriously tempted me for my wedding work and I believe its a great all rounder as well.
2.Sony ergonomics just do not appeal to me. And the fact that by this time next year the next Sony A9 iteration will be out, while Fuji will still be supplying firmware updates to radically improve the GFX.
The A9 and the D850 are both amazing cameras and if I already owned lenses from those manufacturers I'd go for it, but I don't so I won't:) There is always a better camera around the corner so I'd just go with what you enjoy shooting, and for me thats the GFX ( and my X system cameras of course, for anything that moves!).


text:
\section{Qualitative Comparison with Additional Baselines}

\begin{figure}[h!]
    \centering
    \includegraphics[width=0.7\linewidth]{figures/sup-hairfit-easy-small.png}
    \caption{Qualitative comparison with HairFIT when a source and a target hair have similar poses. Note that we blur the face of the images from the K-hairstyle dataset due to the privacy issue.}
    \label{fig:hairfit-easy}
\end{figure}

As stated in our main paper, HairFIT~\cite{chung2021hairfit} proposes a pose-invariant hairstyle transfer model via flow-based hair warping and high-quality multi-view datasets.
Also, StyleFusion~\cite{kafri2021stylefusion} is a recently-proposed generative model which is capable of editing local features of an image (e.g., hairstyle in a facial image) by learning disentanglement of semantic regions in the StyleGAN~\cite{karras2019stylegan} latent space.
We conducted additional qualitative evaluation to demonstrate our superiority over HairFIT and StyleFusion.

\begin{figure}[t!]
    \centering
    \includegraphics[width=\linewidth]{figures/sup-hairfit-hard-small.png}
    \caption{Qualitative comparison with HairFIT when a source and a target hair have different poses. The last two columns present zoomed-in regions of interest, each corresponding area indicated in the third and fourth columns. (Best viewed in color.) The second last column and the last column contain regions of hair texture and occluded regions in the source, respectively. Note that we blur the face of the images from the K-hairstyle dataset due to the privacy issue.}
    \label{fig:hairfit-hard}
\end{figure}

First, we compare our model with HairFIT.
We trained HairFIT in the same way described in the original paper and utilized the K-hairstyle dataset~\cite{kim2021k}.
The implementation codes and the dataset are provided by the authors of HairFIT.
K-hairstyle~\cite{kim2021k} includes 500,000 high-resolution multi-view hairstyle images with more than 6,400 identities.
Following HairFIT, we filtered the images to remove the ones whose hairstyle is significantly occluded, or whose face is extremely rotated.
The training set consists of 37,602 images with 4,291 identities, and the test set contains 4,309 images with 498 identities.
We cropped each image based on its hair and face segmentation mask and resized the images into $256\times256$ for a fair comparison.
For the embedding step of our framework, we trained StyleGAN2~\cite{karras2020stylegan2} with the same dataset before the inference.

Fig.~\ref{fig:hairfit-easy} and Fig.~\ref{fig:hairfit-hard} present the results with similar poses and with large pose differences, respectively.
Fig.~\ref{fig:hairfit-easy} illustrates that HairFIT achieves comparable performance to ours when a target hair is well aligned with a source image.
However, according to Fig.~\ref{fig:hairfit-hard}, HairFIT produces unrealistic outputs where a source and a target hair have different poses.
To be specific, HairFIT could not preserve the texture of straight strands of the target hairstyles, as shown in the fifth column of Fig.~\ref{fig:hairfit-hard}.
Moreover, HairFIT inpaints occluded regions, such as cheeks next to hair, neck, and shoulders, of a source image with undesirable blurry artifacts, as in the last column of Fig.~\ref{fig:hairfit-hard}.

Additionally, we conduct a qualitative comparison with StyleFusion.
We implemented the model with the official codes and utilized FFHQ dataset~\cite{karras2019stylegan} for the comparison.
Following the approach proposed in StyleFusion, we edit the `hair' attribute in the StyleGAN2 latent space to perform hairstyle transfer.
As in Fig.~\ref{fig:stylefusion}, StyleFusion is not shown to properly preserve the detailed textures as well as shapes of the target hairstyle.
We speculate that the entangled attributes in the latent space (\textit{i.e.}, hair and inner face) prevent the model from producing fine details of the hair.

\begin{figure}[t!]
  \centering
  \includegraphics[width=\linewidth]{figures/sup-stylefusion-small.png}
  \caption{Qualitative comparison with StyleFusion.}
  \label{fig:stylefusion}
\end{figure}

\begin{table}[t!]
    \centering
    \begin{tabular}{>{\centering}p{0.12\textwidth}|>{\centering}p{0.18\textwidth}|>{\centering}p{0.25\textwidth}|>{\centering}p{0.25\textwidth}||>{\centering\arraybackslash}p{0.10\textwidth}}
    \toprule
    \makecell{ Ablated \\ Version} &
    \makecell{ Target Hair \\ Alignment }  & \makecell{Semantic Label of \\ Occlusion in $\mathbf{S}^{obj}_{src}$}  & \makecell{ $w^{inpaint}_{src}$ \\ Optimization } &  FID$_{\downarrow}$ \\
    \midrule
    (a) & \xmark & \xmark & in Blending & 43.37 \\
    (b) & \cmark & \xmark & in Blending & 39.68 \\
    (c) & \cmark & \cmark & in Blending & 32.69 \\
    Ours & \cmark & \cmark & in Source Inpainting & \textbf{18.02} \\
    \bottomrule
    \end{tabular}%
    \caption{Quantitative comparison with the ablated versions of our framework. (a), (b), and (c) indicate each ablated version, respectively.}
    \label{Table:sup-ablation}
\end{table}

\begin{figure}[t!]
    \centering
    \includegraphics[width=1.0\linewidth]{figures/sup-ablation-module-small.png}
    \caption{Qualitative comparison with the ablated versions of our framework using the FFHQ dataset. (a), (b), and (c) indicate each ablated version, respectively.}
    \label{fig:sup-ablation}
\end{figure}

\section{Additional Ablation Study}
To present the advantage of each step in our framework, we conduct additional quantitative and qualitative ablation studies using the FFHQ dataset.
For the quantitative evaluation, we measure the fréchet inception distance (FID) score~\cite{heusel2017fid}.

Starting from the embedding and blending step only, we gradually add each step to compare the corresponding results.
In Table~\ref{Table:sup-ablation} and Fig.~\ref{fig:sup-ablation}, we perform only the embedding and blending step in (a), append the target hair alignment step in (b), and add a semantic label of occluded regions to $\mathbf{S}^{obj}_{src}$ as a guide for the source inpainting in (c).
Lastly, in the last row, we include the source inpainting step, an independent optimization step for inpainting, which indicates our full framework.
Note that the source inpainting of (a), (b), and (c) is performed in the blending step, not in the independent source inpainting step.

According to Table~\ref{Table:sup-ablation}, the FID score gradually decreases as we add each step of our framework.
Since (a) does not have the target hair alignment step and a proper guide for the source inpainting, the corresponding outputs show dissatisfying quality.
The third column of Fig.~\ref{fig:sup-ablation} illustrates the results with misaligned hair and unrealistic occlusion inpainting.
Although (b) achieves the improved FID score with the aid of the target hair alignment step, source occlusions of the outputs are filled with unnatural textures, as presented in the fourth column of Fig.~\ref{fig:sup-ablation}.
Since a lack of semantic label of occluded regions in $\mathbf{S}^{obj}_{src}$ cannot provide a proper guide for the source inpainting, (b) allows the occluded regions to be inpainted with random undesirable textures.
On the other hand, (c) produces the results with advanced quality, especially in the regions of source occlusion, with an appropriate assist of $\mathbf{S}^{obj}_{src}$.
However, the fifth column of Fig.~\ref{fig:sup-ablation} indicates that the final outputs include regions inpainted with undesirable colors or textures.
This is because the source inpainting, \textit{i.e.}, the optimization of $w^{inpaint}_{src}$, is conducted simultaneously with the optimization of a blending weight $w^{weight}$ in blending step.

To address this issue, we added an independent $w^{inpaint}_{src}$ optimization step only for source inpainting in our final framework, which achieves superior performance both quantitatively and qualitatively.
The last column of Fig.~\ref{fig:sup-ablation} presents the results of our full framework with a superior quality of target hair alignment and occlusion inpainting compared to other configurations.

\begin{figure}[t]
  \centering
  \includegraphics[width=\linewidth]{figures/sup-qualitative-module-small.png}
  \caption{(a) Visual comparison with InterFaceGAN on the alignment module and (b) qualitative comparison with baselines equipped with our alignment module.}
  \label{fig:qualitative}
\end{figure}

\section{Analysis of Individual Modules}
In this section, we further analyze each module of our framework.
First, we conduct an additional evaluation on the target hair alignment module (HA).
As a baseline, we adopt InterFaceGAN~\cite{shen2020interpreting} which has the capability of aligning the target pose similar to a source via latent vector interpolation.
Fig.~\ref{fig:qualitative}(a) demonstrates the qualitative result on alignment, where the target hair is manipulated to show the same objective pose.
The objective pose is selected by randomly interpolating the target hair along the pose boundary of InterFaceGAN.
InterFaceGAN shows high performance on pose alignment but inappropriately alters the target hairstyle.
In contrast, our model properly produces a pose-aligned image while maintaining the target hair details.

Next, we evaluate the rest of our modules except for HA, by combining HA with the baselines: LOHO and Barbershop.
To this end, we perform a user study with 20 graduate students, to compare 20 images generated by three different configurations: HA + LOHO, HA + Barbershop, and ours.
For each pair, a participant is asked to select a top-1 sample with two criterion: (1) preservation of delicate features of a target hair and (2) inpainting quality against source occlusion.
The study results show that 71\% and 63\% of our method results were selected as the top-1 sample for each criteria, respectively.
Fig.~\ref{fig:qualitative}(b) shows qualitative examples appeared in the user study.
As can be seen, our model visually outperforms the baselines by successfully preserving the texture and shape of a target hair.

\section{Implementation Details}
The optimization step size of embedding $W+$, embedding $FS$, target hair alignment, source inpainting, and blending are 1,100, 250, 100, 140, and 400, respectively.
In the target hair alignment and blending step, we set all lambdas of the losses as 1.

The optimization is conducted on a single GeForce RTX 3090 GPU and it requires 10GB GPU memory.
For the inference time, the embedding step takes less than 2 minutes per image, and all the other steps take 78 seconds on average in total.

\begin{figure}[t!]
    \centering
    \includegraphics[width=0.95\linewidth]{figures/sup-overview-small.png}
    \caption{Visualization of results from each step in our framework.}
    \label{fig:sup-overview}
\end{figure}

\begin{figure}[t!]
    \centering
    \includegraphics[width=0.95\linewidth]{figures/sup-qualitative-small.png}
    \caption{Additional qualitative results of our framework with the FFHQ dataset.}
    \label{fig:sup-qualitative}
\end{figure}

\section{Additional Qualitative Results}
First, to further understand our framework, we visualize an example qualitative result with its intermediate outputs from each step in Fig.~\ref{fig:sup-overview}.
To be specific, given the source image $I_{src}$ and the target hair image $I_{trg}$, $I_{w_{trg}}$, $I_{w_{src}}$, and $I_{{FS}_{src}}$ are the images reconstructed from the embedded latent codes $w_{trg}$, $w_{src}$, and ${FS}_{src}$ obtained in the embedding step.
Also, $I_{w^{align}_{trg}}$ is the aligned target hair image generated from $w^{align}_{trg}$ obtained in the target hair alignment step.
Then, in the source inpainting step, we first create an objective label $S^{obj}_{src}$ for source inpainting based on $S^{keep}_{src}$ from $I_{src}$ and $S^{align}_{trg_hair}$ from $I_{w^{align}_{trg}}$.
By optimizing $w_{src}$ to follow $S^{obj}_{src}$, we obtain inpainted source latent code $w^{inpaint}_{src}$, which is visualized in $I_{w^{inpaint}_{src}}$.
Finally, the final output $\hat{I}$ is generated via the blending step, where we blend $w^{align}_{trg}$ and other features in $w_{src}$ and $w^{inpaint}_{src}$.

Additionally, Fig.~\ref{fig:sup-qualitative} presents additional qualitative results with FFHQ dataset.
We transfer various target hairstyles on the first row of Fig.~\ref{fig:sup-qualitative} to each of the source images in the first column of Fig.~\ref{fig:sup-qualitative}.

\end{document}

\section{Introduction}
With the advance of conditional generative adversarial networks (GANs)~\cite{goodfellow2014generative,odena2017conditional,jo2019sc}, editing facial attributes has drawn great attention and shows a promising result on editing multiple attributes.
Despite the success, modifying strongly correlated facial attributes is still challenging, often beyond the capacity of existing editing models.
In this paper, we focus on hairstyle editing, which aims at transferring a target hairstyle to a source image, proposing high-performance neural networks to solve the problem.
Hairstyle editing is similar to that of a facial attribute, but it has unique, challenging aspects: (1) Due to the hairstyle's complexity and delicacy,  preserving its strands given an arbitrary hairstyle is highly demanding. (2) Transferred hairstyle requires to be exactly fitted to a given source image.
These challenges make the previous approaches for editing the specified facial attributes less suitable for this problem.

Recent solutions for hairstyle transfer address the problem with the power of a pre-trained image generator.
For example, LOHO~\cite{saha2021loho} and Barbershop~\cite{zhu2021barbershop} largely enhance the visual quality of the generated images via latent optimization based on StyleGAN2~\cite{karras2020stylegan2}.
However, these approaches produce undesirable outputs (See Fig.~\ref{fig:teaser}) when handling a target and source image pair with a significant pose difference.

To the best of our knowledge, HairFIT~\cite{chung2021hairfit} is the only work to address the pose difference issue between a source and target image. HairFIT presents a pose-invariant hairstyle transfer model where a target hairstyle is aligned to a source image pose using a flow-based warping module trained on multi-view datasets such as VoxCeleb~\cite{nagrani2017voxceleb} and K-hairstyle dataset~\cite{kim2021k}.
Although its attempt, HairFIT requires a high-quality multi-view hairstyle dataset during training, and it falls behind state-of-the-art models~\cite{saha2021loho,zhu2021barbershop} in light of hair preserving capacity.

In response to these limitations, we present a framework that performs a \textit{high-quality} pose-invariant hairstyle transfer based on latent optimization \textit{without multi-view dataset}.
Specifically, given a source and a target hair image, our model generates a hair-transferred output through embedding, hair pose alignment, inpainting, and blending step.
We first take advantage of GAN-inversion algorithms~\cite{abdal2019image2stylegan,abdal2020image2stylegan++,zhu2020ii2s}, feeding a source and a target hair image, for the purpose of obtaining latent codes residing in the StyleGAN2 space~\cite{karras2020stylegan2}, respectively.
Next, we navigate the StyleGAN2 space to optimize the latent code of the target hair image to follow a source image pose.
During the pose alignment, we utilize a newly-presented local-style-matching loss  to penalize visually degraded hair textures by locally comparing the original target hair with the aligned one.
In the inpainting, we first obtain a segmentation mask to guide the latent code of the source to fill the occluded regions by its hair.
We optimize the latent code of the source image to follow the obtained segmentation mask.
Lastly, we blend the aligned target hairstyle and the inpainted source image with a final optimization step.
In this manner, our model  is able to transfer a target hairstyle to a source image overcoming the difference in poses as well as successfully preserving the fine details of the target hair.
Experiments demonstrate the superiority of our model in a quantitative and qualitative manner.
Our contributions are summarized as follows:
\begin{itemize}
    \item We propose a framework that achieves a \textit{high-quality} pose-invariant hairstyle transfer based on latent optimization \textit{without multi-view dataset}.
     \item We present local-style-matching loss  to maintain the fine details of a hairstyle during pose optimization.
    \item Our model achieves state-of-the-art performance in quantitative and qualitative evaluations with various datasets.
\end{itemize}

\section{Related Work}

\subsection{Latent space manipulation}
With the understanding of the latent space in GANs, recent approaches based on latent space manipulation~\cite{harkonen2020ganspace,shen2020interpreting,viazovetskyi2020distillation} have shown promising results in image editing.
For example, GANSpace~\cite{harkonen2020ganspace} and InterfaceGAN~\cite{shen2020interpreting} modify facial attributes via manipulation in the latent space of StyleGAN~\cite{karras2019stylegan}.
While the former takes advantage of principal component analysis, the latter utilizes semantic scores to identify disentangled directions related to the target attributes.
In a similar manner, Viazovetskyi et al.~\cite{viazovetskyi2020distillation} and Zhuang et al.~\cite{zhuang2021enjoy} attempt to edit the images by shifting latent vectors to semantically meaningful directions in the latent space of StyleGAN2~\cite{karras2020stylegan2}, which can easily be obtained by a pre-trained face classifier or learned transformations.

Recent hairstyle transfer approaches also actively utilize latent space manipulation to synthesize high-quality images.
LOHO~\cite{saha2021loho} and Barbershop~\cite{zhu2021barbershop} edit hairstyles by manipulating the extended StyleGAN2 latent space~\cite{zhu2020ii2s} via latent optimization.
These methods not only significantly enhance the visual quality of the generated images but also preserve the semantic details of the target images.
In particular, Barbershop introduces $FS$ space with a larger capacity than the original StyleGAN2 latent space, where the original hair structure is well-preserved.
In this work, we leverage latent optimization to reach the photo-realistic image quality.
Our model mainly focuses on the pose alignment of a target hairstyle to a source image without losing its detailed hair texture based on local-style-matching loss  to achieve a pose-invariant hairstyle transfer.

\subsection{Hairstyle Transfer}

GAN-based facial image editing~\cite{jiang2020psgan,jo2019sc,lee2020maskgan,portenier2018faceshop,xiao2021sketchhairsalon,yang2020deep} successfully modifies the target facial attributes such as a facial expression or makeup style while maintaining other features.
Common approaches for facial image editing are to utilize hand-drawn sketches~\cite{jo2019sc,portenier2018faceshop,xiao2021sketchhairsalon,yang2020deep} or user-edited semantic masks\cite{lee2020maskgan} as the conditions to precisely guide the manipulated appearance.

In spite of the remarkable progress in facial image editing, hairstyle transfer is still tricky, considering the diversity and intricacy of hairstyles.
In practice, a hairstyle transfer is required to convey a wide range of target hairstyles to a given source image while preserving their subtle hair strands and color.
As a prior work, MichiGAN~\cite{tan2020michigan} presents a hairstyle transfer framework aiming to preserve the detailed textures of a target hairstyle.
Specifically, MichiGAN leverages different conditional generators responsible for decomposed hairstyle attributes (\textit{i.e.}, hair shape, and appearance).
Moreover, LOHO~\cite{saha2021loho} achieves visually pleasant image quality through latent optimization and hair-related losses for reflecting a target hairstyle features.
Barbershop~\cite{zhu2021barbershop} also proposes a latent optimization approach and further improves the visual quality of the outputs based on the newly presented $FS$ space.
Barbershop utilizes $F$ tensor in $FS$ space to enhance the capability of preserving the overall structure of a target image, including delicate hair structure.

However, since the existing approaches have been developed to handle the images, where the head poses of a source and a target are aligned, they show limited generalization capacity for dealing with the inputs having a large pose difference.
To tackle this problem, HairFIT~\cite{chung2021hairfit} introduces a pose-invariant hairstyle transfer with flow-based target hair warping and semantic-region-aware inpainting.
HairFIT leverages an optical flow estimation network and a multi-view hairstyle dataset~\cite{kim2021k} to align the target hair to the source face.
Despite the aid of the high-quality multi-view dataset, the model fails to preserve the detailed features of hairstyles comprehensively.
In this paper, we propose a novel latent optimization framework for pose-invariant hairstyle transfer to synthesize high-quality images regardless of the pose differences.

\section{Method}

\subsection{Overview}
Our framework consists of several optimization steps described in Fig.~\ref{fig:overview}.
We first find latent codes $w \in \mathbb{R}^{18 \times 512}$ of a source image $\mathbf{I}_{src} \in \mathbb{R}^{C \times H \times W}$ and a target hairstyle image $\mathbf{I}_{trg}$ in $W$+ space using the existing GAN-inversion algorithms~\cite{zhu2021barbershop,zhu2020ii2s}.
Then, we optimize the target hair latent codes to have the pose aligned to $\mathbf{I}_{src}$.
While aligning the pose, we mainly focus on preserving fine details of the target hair with a newly-presented local-style-matching loss.
Local-style-matching loss allows preserving each local texture in the aligned target hair by matching the corresponding region of a similar style from the original target hair.
For the next step, we inpaint the source regions occluded by its original hair by optimizing the source latent codes.
Lastly, we blend the aligned target hairstyle and the inpainted source image for the final output.

\begin{figure}[t!]
    \centering
    \includegraphics[width=1.0\linewidth]{figures_final/figure-overview-cy-small.png}
    \caption{An overview of our framework. First, we obtain $w_{src}$, $w_{trg}$, and $F_{src}$ by embedding a source and a target hair image into $W+$ and $FS$ space. Then, we optimize $w_{trg}$ to follow the source pose, resulting in $w^{align}_{trg}$. With the segmentation mask of aligned target hair, we find $w^{inpaint}_{src}$, where the source occlusions are inpainted. Finally, we blend $F_{src}$, $w^{inpaint}_{src}$, and $w^{align}_{trg}$ to generate the final output.}
    \label{fig:overview}
\end{figure}

\subsection{Embedding}
First of all, we obtain the latent codes of each reference image (\textit{i.e.}, source and target images) before pose alignment and blending.
Given a source image $\mathbf{I}_{src}$ and a target hair image $\mathbf{I}_{trg}$, we find the source latent codes $w_{src}$ and the target latent codes $w_{trg}$ in an extended latent space of StyleGAN2 denoted as $W+$ space~\cite{abdal2019i2s}.
We employ an improved embedding algorithm~\cite{zhu2020ii2s} to enhance the reconstruction and editing quality.
Moreover, we embed $\mathbf{I}_{src}$ to $FS$ space following Barbershop~\cite{zhu2021barbershop} to gain $\mathbf{F}_{src} \in \mathbb{R}^{32 \times 32 \times 512}$, which preserves the detailed structure of the source image by encoding the spatial information.

\subsection{Target Hair Alignment}
To transfer the hairstyle regardless of the pose differences, we align the target hairstyle to the source face via the latent optimization, as presented in Fig.~\ref{fig:align}.
Starting from $w_{trg}$, we aim to find $w^{align}_{trg}$, where the head pose and face shape are aligned to $\mathbf{I}_{src}$, while other features, especially the hairstyle, correspond to $\mathbf{I}_{trg}$.
We optimize the first $m$ style vectors among 18 style vectors of $w_{trg}$ to optimize coarse style vectors rather than fine style vectors~\cite{karras2019stylegan}.
We set $m$ as 6 in our experiments.
\begin{figure}[t!]
    \centering
    \includegraphics[width=1.0\linewidth]{figures_final/figure-align-small.png}
    \caption{Target hair alignment. We obtain the aligned target hair latent codes $w^{align}_{trg}$ by optimizing the first $m$ vectors of $w_{trg}$ to have a source pose with its hairstyle preserved.}
    \label{fig:align}
\end{figure}

\noindent\textbf{Pose Align Loss.}
To modify $w_{trg}$ to have a source pose, we propose a novel pose align loss $\mathcal{L}_{pose}$ based on 3D facial keypoints.
Since the hairstyle significantly depends on other facial features (\textit{e.g.}, face shape and location of eyes), the head pose alone is insufficient to fully guide the target hair alignment.
Thus, we leverage 3D facial keypoints, which effectively represent the overall facial features as well as the head pose.
With the source 3D facial keypoints, we can provide detailed supervision of which shape and pose ${w_{trg}}$ should pursue.

$\mathcal{L}_{pose}$ computes the L2 distance between the 3D keypoint heatmaps of $\mathbf{I}_{src}$ and the aligned target hair image as:
\begin{equation}
    \mathcal{L}_{pose} = \frac{1}{N_H}\lVert \mathbf{H}_{src} - E(G(\hat{w_{trg}})) \rVert^{2}_{2}.
\end{equation}
$N_H$ indicates the number of elements in a 3D keypoint heatmap $\mathbf{H} \in \mathbb{R}^{68 \times H \times W}$ and $\mathbf{H}_{src}=E(\mathbf{I}_{src})$, where $E$ is a pre-trained keypoint extractor~\cite{bulat2017far}. $G$ is a pre-trained StyleGAN2 generator and $\hat{w_{trg}}$ indicates the optimized $w_{trg}$ in progress.

\noindent\textbf{Local-Style-Matching Loss.}
To preserve locally distinct hairstyles, we newly present a local-style-matching loss, which matches similar local styles between the target hair and the aligned target hair.
Basically, we utilize a style loss based on the Gram matrix~\cite{gatys2016image}, which captures the repeated patterns (\textit{i.e.}, texture) of given features.
A style loss $\mathcal{L}_{style}$ measures the L2 distance between the Gram matrix of feature maps extracted by a $\mathrm{VGG}$ network~\cite{simonyan2014very}, formulated as:
\begin{equation}
   \mathcal{L}_{style}(\cdot,\cdot) = \frac{1}{V}\sum_{i=1}^V \frac{1}{N_{\mathcal{G}^i}} \lVert \mathcal{G}^i(\mathrm{VGG}^i(\cdot)) - \mathcal{G}^i(\mathrm{VGG}^i(\cdot))\rVert^{2}_{2},
\end{equation}
where $V$ indicates the number of $\mathrm{VGG}$ layers we use, which are $relu1\_2$, $relu2\_2$, $relu3\_3$, and $relu4\_3$ layer of $\mathrm{VGG}$~\cite{chung2021hairfit,saha2021loho,zhu2021barbershop}.
Also, $N_{\mathcal{G}^i}$ represents the number of elements in $\mathcal{G}^{i}$.
Here, $\mathcal{G}^{i}$ and $\mathrm{VGG}^{i}$ indicate the $i$-th Gram matrix and $i$-th layer of $\mathrm{VGG}$, respectively.
$\mathcal{G}^{i}$ is calculated as ${v^{i}}^{\intercal}v^{i}$, where $v^{i} \in \mathbb{R}^{H^{i}W^{i} \times N_{C^{i}}}$ corresponds to the activation of $\mathrm{VGG}^{i}$.

\begin{figure}[t!]
    \centering
    \includegraphics[width=1.0\linewidth]{figures_final/figure_slic-small.png}
    \caption{Local-style-matching loss. During target hair alignment, a local-style-matching loss is applied to style regions in the target hair and those in the aligned target hair. The white boundary regions are segmented style regions, and the red boundary regions describe an example of a consistently tracked style region.}
    \label{fig:lsm}
\end{figure}

In local-style-matching loss $\mathcal{L}^{LSM}_{style}$, we first identify \textit{style regions} each of which includes locally different style and apply $L_{style}$ to each style region, respectively.
To identify the style regions, we leverage a simple linear iterative clustering (SLIC)~\cite{achanta2012slic}.
The SLIC is an algorithm that conducts a K-means clustering based on the similarity of color and spatial distance between pixels.
Since the SLIC considers both the appearance and location of neighboring pixels, it can successfully segment the target hair into proper style regions.

As presented in the first column of Fig.~\ref{fig:lsm}, we first find the style regions in the hair of $\mathbf{I}_{trg}$.
Then, during the latent optimization, we detect the style regions of $G(\hat{w_{trg}})$ and match each region to the most similar style region of $\mathbf{I}_{trg}$, as shown in the rest columns of Fig.~\ref{fig:lsm}.
In each step, we track the regions of similar style by setting the same label to the region of the closest centroid compared to the previous step.
Fig.~\ref{fig:lsm} shows that an example style region marked with a red boundary is successfully tracked based on the proposed algorithm.
$SLIC_{hair}(\mathbf{I}) \in \{0,1\}^{N_{style} \times H \times W} $ indicates style region masks extracted from a hair region of $\mathbf{I}$ using the SLIC algorithm. Here, $N_{style}$ indicates the number style regions.
We set $N_{style}$ as $5$ in our experiments.
$\mathcal{L}^{LSM}_{style}$ is formulated as:
\begin{equation}
    \mathcal{L}^{LSM}_{style} = \sum_{i=1}^{N_{style}}\mathcal{L}_{style}(SLIC^i_{hair}(\mathbf{I}_{trg}) \odot \mathbf{I}_{trg} , SLIC^i_{hair}(G(\hat{w_{trg}})) \odot G(\hat{w_{trg}}) ).
\end{equation}
$SLIC^i_{hair}(\cdot)$ is the $i$-th channel of $SLIC_{hair}(\cdot)$ and $\odot$ indicates element-wise product.
Note that a valid region of each channel, where the style region mask corresponds to 1, is cropped before calculating the style loss.

\noindent\textbf{Regularization Loss.}
We add a step-wise regularization loss to keep the overall features of $\hat{w_{trg}}$, especially hairstyle, similar to the previous step.
The regularization loss $\mathcal{L}_{reg}$ encourages a stable optimization via a gradual modification without a noticeable loss of the original hairstyle features.
$\mathcal{L}_{reg}$ is formulated as:
\begin{equation}
    \mathcal{L}_{reg} = \frac{1}{N_w}\lVert \Delta \hat{w_{trg}} \rVert^{2}_{2},
\end{equation}
where $N_w$ indicates the number of elements in $w$.
$\Delta \hat{w_{trg}}$ at step $t$ is obtained by $\hat{w_{trg}}^{t} - \hat{w_{trg}}^{t-1}$, where $t$ ranges from 2 to the total number of steps.

Formally, the total objective function in the target hair alignment step is $\mathcal{L}_{pose} + \lambda^{LSM}_{style} \mathcal{L}^{LSM}_{style} + \lambda_{reg} \mathcal{L}_{reg}$, where $\lambda^{LSM}_{style}$ and $\lambda_{reg}$ denote the hyper-parameters to control relative importance between different losses.

\begin{figure}[t!]
    \centering
    \includegraphics[width=1.0\linewidth]{figures_final/figure-inpaint-small.png}
    \caption{Generation of an objective label. For source inpainting, we create an objective label $\mathbf{S}^{obj}_{src}$ to guide the occluded regions to be inpainted with proper semantics.}
    \label{fig:align_mask}
\end{figure}

\subsection{Source Inpainting}
Source inpainting step aims to inpaint the regions occluded by the original source hair.
As shown in Fig.~\ref{fig:overview}, if we remove the source hair region from the source image, the occluded region should be filled with the proper semantics (\textit{e.g.}, forehead, face, neck, clothes, and background) to fit the aligned target hair.

To find the inpainted source latent code $w^{inpaint}_{src}$, we generate an objective label $\mathbf{S}^{obj}_{src} \in \mathbb{Z}^{H \times W}$ to guide the occluded regions to be filled with the appropriate semantic regions.
$\mathbf{S}^{obj}_{src}$ is generated by the following process, as also described in Fig.~\ref{fig:align_mask}.
First, we compute a keep label $\mathbf{S}^{keep}_{src}$, which indicates the regions that need to be maintained in the source, by removing a source hair region $\mathbf{S}_{src\_hair}$ from a source semantic label $\mathbf{S}_{src}$.
Here, $\mathbf{S}_{src}$ is estimated by a pre-trained segmentation network~\cite{yu2018bisenet}.
Next, we calculate a label of regions to be inpainted $\mathbf{S}^{inpaint}$ as described in Fig.~\ref{fig:align_mask}.
Finally, we obtain $\mathbf{S}^{obj}_{src}$ which indicates the inpainting regions of the source image considering the aligned target hair.
Now, we optimize ${w}^{inpaint}_{src}$ to follow the given $\mathbf{S}^{obj}_{src}$.
Here, as in the target hair alignment step, we optimize the first $m$ $w$ vectors to newly generate coarse features to fill the occlusions while preserving the fine details or the overall appearance of the source.
For optimizing ${w}^{inpaint}_{src}$, we use a pixel-wise cross-entropy loss between the label of $\mathbf{S}^{obj}_{src}$ and a segmentation probability heatmap of the generated image, which consists of 16 semantic region categories.
The heatmap is estimated by the pre-trained segmentation network.

\subsection{Blending}
The final optimization step aims to find a blending weight $w^{weight}$ that merges the optimized latent codes from the previous steps to generate the final output.
\begin{figure}[t!]
    \centering
    \includegraphics[width=1.0\linewidth]{figures_final/figure-blending-small.png}
    \caption{Blending. (a) $w$ vectors from the previous steps are blended with the optimized blending weight $w^{weight}$ to obtain $w^{blend}$. (b) Next, we combine $\mathbf{F}^{align}_{trg}$, $\mathbf{F}^{blend}$, and $\mathbf{F}_{src}$ with the corresponding masks to obtain $\mathbf{F}^{final}$. (c) $\mathbf{F}^{final}$ and $w^{blend}$ are fed to the StyleGAN2 generator to synthesize the final output. (d) Blending loss consists of $\mathcal{L}^{keep}_{percept}, \lambda^{hair}_{percept}$, and $\mathcal{L}^{hair}_{percept}$.}
    \label{fig:blending}
\end{figure}
First, as presented in Fig.~\ref{fig:blending}(a), $w^{blend}$ is obtained by blending $w^{inpaint}_{src}$ and $w^{align}_{trg}$ with the blending weight $w^{weight}$.
$w^{blend}$ is formulated as $w^{inpaint}_{src} + w^{weight} \odot w^{align}_{trg}$, where $w^{weight}$ implies how much of $w^{align}_{trg}$ needs to be reflected to synthesize the final output.
Then, we prepare $\mathbf{F}$ tensors by feeding the first $m$ $w$ vectors to the pre-trained StyleGAN2 generator, as shown in Fig.~\ref{fig:blending}(b).
Here, we leverage $\mathbf{F}$ tensors in $FS$ space to effectively reconstruct the detailed spatial information~\cite{zhu2021barbershop} in the further blending.
We blend $\mathbf{F}^{align}_{trg}$, $\mathbf{F}^{blend}$, and $\mathbf{F}_{src}$ to gain $\mathbf{F}^{final}$ which contains detailed spatial information of the final output.
$\mathbf{F}^{final}$ is calculated as follows:
\begin{equation}
    \mathbf{F}^{final} = \mathbf{M}_{trg\_hair}^{align} \odot \mathbf{F}^{align}_{trg} + \mathbf{M}^{blend} \odot \mathbf{F}^{blend} + \mathbf{M}^{keep}_{src} \odot \mathbf{F}_{src}.
\end{equation}
$\mathbf{F}^{align}_{trg}$ and $\mathbf{F}^{blend}$ are extracted from $w^{align}_{trg}$ and $w^{blend}$, respectively, and $\mathbf{F}_{src}$ is from the embedding step.
$\mathbf{M}_{trg\_hair}^{align}$ is a binary mask indicating the hair region in the aligned target hair image. $\mathbf{M}^{keep}_{src}$ is also a binary mask denoting the regions which are neither the source hair nor the aligned target hair. $\mathbf{M}^{keep}_{src}$ indicates the area that needs to be preserved in the source image.
Lastly, $\mathbf{M}^{blend}$ denotes the remaining regions.
The final output $\mathbf{\hat{I}}$ is generated from the pre-trained StyleGAN2 generator given $w^{blend}$ and $\mathbf{F}^{final}$ as inputs. Here, vectors in $w^{blend}$ except the first $m$ vectors are fed to the generator.

\noindent\textbf{Losses.}
In order to blend the previous optimized latent codes while preserving their structure and styles, we utilize the following losses.

First, to maintain the source face, clothes, background, etc., we apply the perceptual loss~\cite{zhang2018perceptual} on the valid regions in $\mathbf{S}^{keep}_{src}$ (\textit{i.e.}, the regions to be preserved in the source image) as follows:
\begin{equation}
    \mathcal{L}^{keep}_{percept} = \frac{1}{V}\sum_{i=1}^V \frac{1}{N_{\mathrm{VGG}^i}}\lVert \mathbf{M}^{keep}_{src} \odot ( \mathrm{VGG}^i(\mathbf{I}_{src}) - \mathrm{VGG}^i(\mathbf{\hat{I}}))\rVert_{1},
\end{equation}
where $\mathrm{VGG}^i$ denotes $i$-th layer of $\mathrm{VGG}16$ network~\cite{simonyan2014very} and $N_{\mathrm{VGG}^i}$ is the number of elements in the activation of $\mathrm{VGG}^{i}$.

Also, in order to preserve the aligned target hairstyle from the aligned target latent code $w^{align}_{trg}$, we use the hair perceptual loss formulated as follows:
\begin{equation}
    \mathcal{L}^{hair}_{percept} = \frac{1}{V}\sum_{i=1}^V \frac{1}{N_{\mathrm{VGG}^i}}\lVert \mathbf{M}^{align}_{trg\_hair} \odot ( \mathrm{VGG}^i(\mathbf{I}^{align}_{trg}) - \mathrm{VGG}^i(\mathbf{\hat{I}}))\rVert_{1}.
\end{equation}

Lastly, we maintain the texture of the original target hair by utilizing the hairstyle loss $\mathcal{L}^{hair}_{style}$, where the style loss $\mathcal{L}_{style}$ is applied on the hair regions of the target hair image and the final output as $\mathcal{L}_{style}(\mathbf{M}_{trg\_hair}\odot\mathbf{I}_{trg},\mathbf{M}_{\hat{I}\_hair}\odot\mathbf{\hat{I}})$.

The total blending loss to optimize $w^{weight}$ is $\mathcal{L}^{keep}_{percept} + \lambda^{hair}_{percept}\mathcal{L}^{hair}_{percept} + \lambda^{hair}_{style}\mathcal{L}^{hair}_{style}$, where $\lambda^{hair}_{percept}$ and $\lambda^{hair}_{style}$ are the hyper-parameters to balance the relative importance between the losses.

\section{Experiments}

\subsection{Experimental Setup}
\textbf{Dataset.}
We utilize Flickr-Faces-HQ (FFHQ) dataset~\cite{karras2019stylegan} for hairstyle transfer and K-hairstyle~\cite{kim2021k} and VoxCeleb2~\cite{Chung18vox2} for reconstruction task.
For hairstyle transfer, we sample 6,000 pairs of two different identities (one for source and the other for target hairstyle) from 70,000 1,024$\times$1,024 images in FFHQ.

For the reconstruction, we create 500 test pairs by sampling the images from the K-hairstyle dataset, which includes 500,000 high-resolution multi-view images with more than 6,400 identities.
Following HairFIT~\cite{chung2021hairfit}, we filtered the images to remove the ones whose hairstyle is significantly occluded, or whose face is extremely rotated.
Additionally, we sample 500 pairs of a source and a target from more than 1 million videos in VoxCeleb2.
In the reconstruction task, two images in each pair have the same identity and different poses, and the source image in each pair is considered the ground truth image for the model to reconstruct. Each image is resized to 256$\times$256 in the experiments.

\noindent
\textbf{Baseline Models.}
We conduct a quantitative and qualitative comparison between our model and the following baselines: LOHO~\cite{saha2021loho}, Barbershop~\cite{zhu2021barbershop}, and HairFIT~\cite{chung2021hairfit}.
Here, we follow the official implementation code of LOHO and Barbershop.
Since LOHO utilizes an external inpainting network, we use a state-of-the-art inpainting network CoModGAN~\cite{zhao2021large}.
Also, we implement HairFIT with the codes and guidelines provided by the authors of HairFIT.

\subsection{Comparison to Baselines}
\textbf{Quantitative evaluations.}
First, we compare the fréchet inception distance (FID) score~\cite{heusel2017fid} of LOHO, Barbershop, and our model on hairstyle transfer task.
The FID score measures how similar the distributions of the synthesized images and the real images are, where the lower FID score indicates a higher similarity.
We compare 6,000 pairs of real and fake images, where each image is resized to 256$\times$256 for the evaluation.
As shown in the last column of Table~\ref{Table:posediff}, we achieve the lowest FID score compared to the baselines.

For further analysis, we compare the FID scores on three different levels of pose difference as conducted in the previous work~\cite{chung2021hairfit,saha2021loho}.
We calculate the pose difference, PD, following the protocol presented in HairFIT~\cite{chung2021hairfit}.
In particular, we use 17 facial jaw keypoints extracted by the pre-trained 3D-keypoint extraction model~\cite{bulat2017far}.
The pose difference is calculated as $\mathrm{PD} = \frac{1}{17}\sum_{i=1}^{17}\rVert \mathbf{k}_{src}^i - \mathbf{k}_{trg}^i \rVert_{1}$, where $\mathbf{k}_{src}^i \in \mathbb{R}^{3}$ is a 3D coordinates of the $i$-th source keypoint and $\mathbf{k}_{trg}^i \in \mathbb{R}^{3}$ is a 3D coordinates of the $i$-th target keypoint.
Then, we divide the 6,000 pairs of a source and a target into three categories of 2,000 pairs: Easy, Medium, and Difficult.
As presented in Table~\ref{Table:posediff}, our model  outperforms the other baselines for Medium and Difficult.
Moreover, the margin between the FID scores of our model and other baselines increases as the pose difference increases from Easy to Difficult.

Additionally, we conduct a comparison with HairFIT on the reconstruction task using K-hairstyle and VoxCeleb2.
As in HairFIT, we measure the structural similarity (SSIM)~\cite{wang2004image} and learned perceptual image patch similarity (LPIPS)~\cite{zhang2018unreasonable} between generated images and ground truth images.
Table~\ref{Table:quant_hairfit} presents that our model outperforms HairFIT (except for LPIPS of VoxCeleb2) \textit{even without} learning to reconstruct different views of a source image using a multi-view dataset.

\begin{table}[!t]
    \centering
    \begin{tabular}{c|c|c|c|c}
    \toprule
    Pose difference level & Easy & Medium & Difficult & Total \\
    \midrule
    LOHO~\cite{saha2021loho} & 21.70 & 23.40 & 28.36 & 19.63 \\
    Barbershop~\cite{zhu2021barbershop} & \textbf{20.75} & 21.45 & 26.30 & 18.07\\
    Ours & 20.79 & \textbf{20.56} & \textbf{22.72} & \textbf{17.06}\\
    \bottomrule

    \end{tabular}%
    \caption{Quantitative comparison with baselines. We measure the FID scores with three different levels of pose difference and with total pairs.}
    \label{Table:posediff}
\end{table}

\begin{table}[t!]
    \centering
    \begin{tabular}{c|cc|cc}
    \toprule
    Dataset & \multicolumn{2}{c|}{K-hairstyle} & \multicolumn{2}{c}{VoxCeleb2} \\
    \midrule
    Metric & SSIM$_{\uparrow}$ & LPIPS$_{\downarrow}$ & SSIM$_{\uparrow}$ & LPIPS$_{\downarrow}$\\
    \midrule
    HairFIT & 0.7242 & 0.2054 & 0.7520 & \textbf{0.2033} \\
    Ours & \textbf{0.7424} & \textbf{0.1786} & \textbf{0.7717} & 0.2078\\
    \bottomrule
    \end{tabular}
    \caption{Quantitative comparisons with HairFIT using multi-view datasets.
    }
    \label{Table:quant_hairfit}
\end{table}

\begin{figure*}[!t]
    \centering
    \includegraphics[width=0.82\linewidth]{figures_final/hard_level-small.png}
    \caption{Qualitative comparison with the baselines on Difficult level of pose difference.}
    \label{fig:hard}
\end{figure*}

\begin{figure*}[!t]
    \centering
    \includegraphics[width=0.82\linewidth]{figures_final/easy_and_medium-small.png
    \caption{Qualitative comparison on (a) Easy and (b) Medium level of pose difference.}
    \label{fig:easy}
\end{figure*}

\noindent\textbf{Qualitative evaluations.}
Fig.~\ref{fig:hard} and Fig.~\ref{fig:easy} demonstrate that our model  successfully transfers the target hairstyle into the source regardless of the pose differences.
Especially, as presented in Fig.~\ref{fig:hard}, our model  shows superiority over other baselines on the Difficult level.
Furthermore, although the FID score of our model  is slightly higher than Barbershop on Easy level, Fig.~\ref{fig:easy} present that the quality of our model  is better to reflect the target hairstyle than the baselines.
The results show that our model  successfully aligns the target hair to the source image, producing high-quality images of hairstyle transfer.
More results of the qualitative comparison are presented in the supplementary materials.

\begin{figure*}[!t]
    \centering
    \includegraphics[width=0.8\linewidth]{figures_final/ablation-small.png
    \caption{Qualitative ablation study on the losses in target hair alignment step.}
    \label{fig:ablation}
\end{figure*}

\begin{table}[t]
    \centering
    \begin{tabular}{c|ccc}
    \midrule
    Configurations &  w/o ($\mathcal{L}_{reg}, \mathcal{L}^{LSM}_{style}$) & w/o $\mathcal{L}^{LSM}_{style}$ & Ours \\
    \midrule
    SSIM$_{\uparrow}$  & 0.7667 & 0.7716 & \textbf{0.7717}\\
    LPIPS$_{\downarrow}$  & 0.2125 & 0.2082 & \textbf{0.2078} \\
    \bottomrule
    \end{tabular}%
    \caption{Quantitative ablation study on the losses in target hair alignment step.}
    \label{Table:quant_abl}
\end{table}

\subsection{Ablation Study}
In the ablation study, we demonstrate the effectiveness of a local-style-matching loss  and regularization loss in our target hair alignment step.
We conduct a qualitative evaluation on hairstyle transfer using the FFHQ dataset and quantitative evaluation on the reconstruction task with VoxCeleb2.
In Fig.~\ref{fig:ablation} and Table~\ref{Table:quant_abl}, \textit{w/o} $(\mathcal{L}_{reg}, \mathcal{L}^{LSM}_{style})$ denotes our framework without $\mathcal{L}_{reg}$ and $\mathcal{L}^{LSM}_{style})$. Also, \textit{w/o} $\mathcal{L}^{LSM}_{style}$ indicates our framework without $\mathcal{L}^{LSM}_{style}$ and \textit{Ours} is our full framework.

The first row of Fig.~\ref{fig:ablation} indicates that the generated target hair is longer than the original target hair due to the absence of the $\mathcal{L}_{reg}$.
In the second row, the direction of the front hair of the outputs without $\mathcal{L}^{LSM}_{hair}$ are different from the original target hairstyle.
Moreover, in the third row, the ``part" of the target hair is better reflected in the output of ours.
The results present that our proposed losses effectively reflect the local style of the target hair while preserving its overall style.
Additionally, as seen in Table~\ref{Table:quant_abl}, our full model outperforms other configurations with a gradual performance increase.
Although the difference between \textit{Ours} and \textit{w/o} $\mathcal{L}^{LSM}_{style}$ is marginal, the qualitative results presented above clearly illustrate the high visual quality of our full model in terms of preserving delicate hair features.
\section{Discussions}
Although our model achieves a state-of-the-art performance compared to the baselines, several challenges still remain.
First, since we transfer hairstyles via online latent optimization, it takes a few minutes on average for each image pair.
Also, our framework cannot newly generate the occluded part of the target hair due to the extremely turned head pose.
For example, the first three columns of Fig.~\ref{fig:limit} show that where the hair on the side is extremely occluded so that the final output barely has side hair.
Finally, the output might contain undesired background when the hair segmentation mask is inaccurately predicted.
The last three columns of Fig.~\ref{fig:limit} present undesirable background leaking.

\section{Conclusions}
This paper proposes a latent optimization framework for high-quality pose-invariant hairstyle transfer via local-style-aware hair alignment.
By leveraging latent optimization, we align the target hair without a multi-view dataset, while maintaining fine details of the hairstyle.
In addition, during the hair alignment, our newly-presented local-style-matching loss encourages our model to preserve the distinct structure and color of each local hair region in detail.
Finally, we perform occlusion inpainting and blending via latent optimization.
In this way, our model produces high-quality final output without noticeable artifacts.

\subsubsection{Acknowledgments.} This work was supported by the Institute of Information \& communications Technology Planning \& Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2019-0-00075, Artificial Intelligence Graduate School Program (KAIST) and the Ministry of Culture, Sports and Tourism and Korea Creative Content Agency (Project Number: R2021040097, Contribution Rate: 50).

redpajama_set_name: RedPajamaArXiv


text:
.. rst-class:: outdated

Models
======

.. danger::

   We're sorry but **this documentation section is outdated**. Please have that in mind when trying to use it.
   You can help us making documentation up to date via Sylius Github. Thank you!

.. _component_channel_model_channel:

Channel
-------

Sale channel is represented by a **Channel** model. It should have everything
concerning channel's data and as default has the following properties:

+-------------+----------------------------------------+
| Property    | Description                            |
+=============+========================================+
| id          | Unique id of the channel               |
+-------------+----------------------------------------+
| code        | Channel's code                         |
+-------------+----------------------------------------+
| name        | Channel's name                         |
+-------------+----------------------------------------+
| description | Channel's description                  |
+-------------+----------------------------------------+
| url         | Channel's URL                          |
+-------------+----------------------------------------+
| color       | Channel's color                        |
+-------------+----------------------------------------+
| enabled     | Indicates whether channel is available |
+-------------+----------------------------------------+
| createdAt   | Date of creation                       |
+-------------+----------------------------------------+
| updatedAt   | Date of update                         |
+-------------+----------------------------------------+

.. note::
   This model implements :ref:`component_channel_model_channel-interface`.

   For more detailed information go to `Sylius API Channel`_.

.. _Sylius API Channel: http://api.sylius.com/Sylius/Component/Channel/Model/Channel.html


Cross Bay Bridge and follow Route 50E approximately 25 miles. Rink is on the right – inside the Talbot County Community Center, across from the Hog Neck Golf Course. Park and enter to the rear of the building.
Follow 301 South to Route 213 South ramp towards Wye Mills/Easton. Turn left onto Route 213. Turn left onto Route 50 East for approximately 10 miles. Rink is on the right – inside the Talbot County Community Center, across from the Hog Neck Golf Course.