{"id":85284,"date":"2020-06-14T19:42:01","date_gmt":"2020-06-14T17:42:01","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/pifu-sistema-mashinnogo-obucheniya-dlya-postroeniya-3d-modeli-cheloveka-na-osnove-2d-snimkov"},"modified":"2020-06-14T19:42:01","modified_gmt":"2020-06-14T17:42:01","slug":"pifu-sistema-mashinnogo-obucheniya-dlya-postroeniya-3d-modeli-cheloveka-na-osnove-2d-snimkov","status":"publish","type":"post","link":"https:\/\/prohoster.info\/fr\/blog\/news\/pifu-sistema-mashinnogo-obucheniya-dlya-postroeniya-3d-modeli-cheloveka-na-osnove-2d-snimkov","title":{"rendered":"PIFu \u2014 un syst\u00e8me d'apprentissage automatique pour cr\u00e9er un mod\u00e8le 3D d'une personne \u00e0 partir de photos 2D.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Un groupe de chercheurs de plusieurs universit\u00e9s am\u00e9ricaines a publi\u00e9 un projet PIFu<\/a><\/noindex> (Fonction implicite align\u00e9e sur les pixels), qui permet d'appliquer des m\u00e9thodes d'apprentissage automatique pour cr\u00e9er un mod\u00e8le 3D d'une personne \u00e0 partir d'une ou plusieurs images 2D. Le syst\u00e8me permet de recr\u00e9er des variantes complexes de v\u00eatements, telles que des jupes pliss\u00e9es et des chaussures \u00e0 talons, ainsi que diff\u00e9rentes coiffures, en reconstruisant automatiquement la texture et la forme dans les zones invisibles dans la projection \u00e0 partir de laquelle le mod\u00e8le 3D est g\u00e9n\u00e9r\u00e9. Pour am\u00e9liorer la qualit\u00e9 et le d\u00e9tail du mod\u00e8le 3D final, plusieurs images sous diff\u00e9rents angles peuvent \u00eatre utilis\u00e9es. Le code du projet est \u00e9crit en Python en utilisant le cadre PyTorch et est distribu\u00e9<\/a><\/noindex> sous licence MIT.<\/p>\n

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

Comme source pour la reconstruction du mod\u00e8le volum\u00e9trique, un r\u00e9seau neuronal est utilis\u00e9, permettant de choisir la forme la plus probable et de compl\u00e9ter les \u00e9l\u00e9ments cach\u00e9s, en se basant sur un mod\u00e8le entra\u00een\u00e9 sur diff\u00e9rentes variantes d'objets existants. Parall\u00e8lement, le projet fournit un algorithme pour faire correspondre le mod\u00e8le volum\u00e9trique obtenu avec les textures des images 2D fournies, qui aligne les pixels de l'image 2D en fonction de leur position sur l'objet 3D et g\u00e9n\u00e8re les textures manquantes les plus probables. Pour le codage de l'image, n'importe quel r\u00e9seau neuronal convolutionnel<\/a><\/noindex>, pour
\ndes surfaces a \u00e9t\u00e9 appliqu\u00e9e l'architecture \u00abStacked hourglass<\/a><\/noindex>\u00bb, et
\n pour la correspondance des textures, un r\u00e9seau neuronal bas\u00e9 sur l'architecture CycleGAN<\/a><\/noindex>.<\/p>\n

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

Le mod\u00e8le pr\u00e9-entra\u00een\u00e9 utilis\u00e9 par les chercheurs disponible<\/a><\/noindex> est disponible au t\u00e9l\u00e9chargement libre, mais les donn\u00e9es d'origine sur lesquelles l'entra\u00eenement a \u00e9t\u00e9 effectu\u00e9 restent priv\u00e9es, car elles sont bas\u00e9es sur des r\u00e9sultats de scan 3D commerciaux. Comme source pour entra\u00eener le mod\u00e8le par soi-m\u00eame, une base de mod\u00e8les 3D<\/a><\/noindex> de personnes du projet Renderpeople.<\/p>\n

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

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

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

\u0413\u0440\u0443\u043f\u043f\u0430 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u0435\u0439 \u0438\u0437 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0430\u043c\u0435\u0440\u0438\u043a\u0430\u043d\u0441\u043a\u0438\u0445 \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u043e\u0432 \u043e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043b\u0430 \u043f\u0440\u043e\u0435\u043a\u0442 PIFu (Pixel-Aligned Implicit Function), \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0438\u0439 \u043f\u0440\u0438\u043c\u0435\u043d\u0438\u0442\u044c \u043c\u0435\u0442\u043e\u0434\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u044f 3D-\u043c\u043e\u0434\u0435\u043b\u0438 \u0447\u0435\u043b\u043e\u0432\u0435\u043a\u0430 \u043f\u043e \u043e\u0434\u043d\u043e\u043c\u0443 \u0438\u043b\u0438 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