{"id":38674,"date":"2019-10-31T22:25:15","date_gmt":"2019-10-31T19:25:15","guid":{"rendered":"https:\/\/prohoster.info\/blog\/otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej\/"},"modified":"2019-10-31T22:25:15","modified_gmt":"2019-10-31T19:25:15","slug":"otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej","status":"publish","type":"post","link":"https:\/\/prohoster.info\/fr\/blog\/news\/otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej","title":{"rendered":"Le code pour la synth\u00e8se d'animations \u00e0 l'aide de r\u00e9seaux de neurones est ouvert","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Un groupe de chercheurs de l'universit\u00e9 technique de Shanghai a publi\u00e9<\/a><\/noindex> outils Impersonator<\/a><\/noindex>, qui permet de simuler les mouvements des personnes sur des images statiques \u00e0 l'aide de techniques d'apprentissage automatique, ainsi que de remplacer les v\u00eatements, de les transf\u00e9rer dans un autre environnement et de modifier l'angle de vue de l'objet. Le code est \u00e9crit en Python
\nen utilisant le framework PyTorch<\/a><\/noindex>. Pour cela, il faut \u00e9galement torchvision<\/a><\/noindex> et le CUDA Toolkit. <\/p>\n

\"Le<\/center><\/p>\n

L'outil re\u00e7oit en entr\u00e9e une image en deux dimensions et synth\u00e9tise un r\u00e9sultat modifi\u00e9 sur la base du mod\u00e8le choisi. Trois types de transformations sont pris en charge :
\nCr\u00e9ation d'un objet en mouvement imitant les mouvements sur lesquels le mod\u00e8le a \u00e9t\u00e9 entra\u00een\u00e9. Transfert des \u00e9l\u00e9ments d'apparence du mod\u00e8le \u00e0 l'objet (par exemple, changement de v\u00eatements). G\u00e9n\u00e9ration d'un nouvel angle de vue (par exemple, synth\u00e8se d'une image de profil \u00e0 partir d'une photo de face). Les trois m\u00e9thodes peuvent \u00eatre combin\u00e9es, par exemple, on peut g\u00e9n\u00e9rer une vid\u00e9o \u00e0 partir d'une photo, imitant l'ex\u00e9cution d'un mouvement acrobatique complexe dans d'autres v\u00eatements. <\/p>\n

\r\n \"Lire\r\n
<\/div>\r\n <\/div><\/center><\/p>\n

Au cours de la synth\u00e8se, des op\u00e9rations d'extraction de l'objet sur la photo et de compl\u00e9tion des \u00e9l\u00e9ments de fond manquants lors du d\u00e9placement sont \u00e9galement effectu\u00e9es. Le mod\u00e8le pour le r\u00e9seau de neurones peut \u00eatre entra\u00een\u00e9 une fois et utilis\u00e9 pour diverses transformations. Pour le chargement, sont disponibles<\/a><\/noindex> des mod\u00e8les pr\u00eats \u00e0 l'emploi permettent d'utiliser imm\u00e9diatement l'outil sans entra\u00eenement pr\u00e9alable. Un GPU avec au moins 8 Go de m\u00e9moire est requis.<\/p>\n

Contrairement aux m\u00e9thodes de transformation bas\u00e9es sur des transformations par points cl\u00e9s d\u00e9crivant la position du corps dans l'espace en deux dimensions, l'Impersonator tente de synth\u00e9tiser un maillage tridimensionnel (mesh) d\u00e9crivant le corps, en utilisant des m\u00e9thodes d'apprentissage automatique.
\nLa m\u00e9thode propos\u00e9e permet de manipuler en tenant compte de la forme corporelle personnalis\u00e9e et de la pose actuelle, en mod\u00e9lisant des mouvements naturels des membres. <\/p>\n

\"Le<\/a><\/noindex><\/center><\/p>\n

Pour pr\u00e9server les informations d'origine telles que les textures, le style, les couleurs et la reconnaissabilit\u00e9 du visage, une r\u00e9seau de neurones antagoniste g\u00e9n\u00e9ratif<\/a><\/noindex> (Liquid Warping GAN<\/a><\/noindex>). Les informations sur l'objet d'origine et les param\u00e8tres pour son identification pr\u00e9cise sont extraites \u00e0 l'aide d'une r\u00e9seau de neurones convolutif<\/a><\/noindex>.<\/p>\n

\r\n \"Lire\r\n
<\/div>\r\n <\/div>
\n<\/center><\/p>\n

<\/a><\/noindex><\/p>\n

Source : opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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\u0442\u0440\u0435\u0431\u0443\u0435\u0442\u0441\u044f torchvision \u0438 CUDA Toolkit. \u0418\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0439 \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u0442 […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":29010,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-38674","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47Code ouvert pour la synth\u00e8se d'animations \u00e0 l'aide de r\u00e9seaux de neurones | ProHoster","description":"Un groupe de chercheurs de l'Universit\u00e9 technologique de 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