{"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\/hu\/blog\/news\/otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej","title":{"rendered":"Megny\u00edlt a k\u00f3d az anim\u00e1ci\u00f3k mesters\u00e9ges intelligencia \u00e1ltali szint\u00e9zis\u00e9hez.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

A sanghai m\u0171szaki egyetem kutat\u00f3csoportja kiadta<\/a><\/noindex> eszk\u00f6zk\u00e9szlet Impersonator<\/a><\/noindex>, amely lehet\u0151v\u00e9 teszi a g\u00e9pi tanul\u00e1si m\u00f3dszerek seg\u00edts\u00e9g\u00e9vel az emberek mozg\u00e1s\u00e1nak szimul\u00e1l\u00e1s\u00e1t statikus k\u00e9peken, valamint a ruh\u00e1k cser\u00e9j\u00e9t, a k\u00f6rnyezet \u00e1thelyez\u00e9s\u00e9t \u00e9s a n\u00e9z\u0151pont megv\u00e1ltoztat\u00e1s\u00e1t. A k\u00f3d Python nyelven \u00edr\u00f3dott
\na PyTorch<\/a><\/noindex>keretrendszer haszn\u00e1lat\u00e1val torchvision<\/a><\/noindex> \u00e9s CUDA Toolkit. <\/p>\n

\"Megny\u00edlt<\/center><\/p>\n

Az eszk\u00f6zk\u00e9szlet bemeneti adatai egy k\u00e9tdimenzi\u00f3s k\u00e9p, \u00e9s az eredm\u00e9nyt a kiv\u00e1lasztott modell alapj\u00e1n szintetiz\u00e1lja. H\u00e1romf\u00e9le transzform\u00e1ci\u00f3 t\u00e1mogatott:
\nMozg\u00f3 objektum l\u00e9trehoz\u00e1sa, amely azokon a mozg\u00e1sokon alapul, amelyeken a modell tanult. A modell k\u00fcls\u0151 megjelen\u00e9s\u00e9nek elemeinek \u00e1tad\u00e1sa az objektumnak (p\u00e9ld\u00e1ul ruhacsere). \u00daj n\u00e9z\u0151pont gener\u00e1l\u00e1sa (p\u00e9ld\u00e1ul profilk\u00e9pek szintetiz\u00e1l\u00e1sa szemb\u0151l k\u00e9sz\u00fclt f\u00e9nyk\u00e9pek alapj\u00e1n). Mindh\u00e1rom m\u00f3dszer kombin\u00e1lhat\u00f3, p\u00e9ld\u00e1ul egy f\u00e9nyk\u00e9pr\u0151l \u00fagy lehet vide\u00f3t gener\u00e1lni, amely bonyolult akrobata tr\u00fckk\u00f6k v\u00e9grehajt\u00e1s\u00e1t imit\u00e1lja m\u00e1s ruh\u00e1ban. <\/p>\n

\r\n \"Vide\u00f3\r\n
<\/div>\r\n <\/div><\/center><\/p>\n

A szint\u00e9zis folyam\u00e1n a f\u00e9nyk\u00e9pen tal\u00e1lhat\u00f3 objektum kiemel\u00e9se \u00e9s a h\u00e1tt\u00e9r hi\u00e1nyz\u00f3 elemeinek l\u00e9trehoz\u00e1sa is megt\u00f6rt\u00e9nik az \u00e1thelyez\u00e9s sor\u00e1n. A neur\u00e1lis h\u00e1l\u00f3zathoz sz\u00fcks\u00e9ges modell egyszeri tan\u00edt\u00e1s ut\u00e1n k\u00fcl\u00f6nb\u00f6z\u0151 transzform\u00e1ci\u00f3khoz felhaszn\u00e1lhat\u00f3. K\u00e9szen \u00e1ll\u00f3 modellek t\u00f6lthet\u0151k le, amelyek lehet\u0151v\u00e9 teszik az eszk\u00f6zk\u00e9szlet azonnali haszn\u00e1lat\u00e1t el\u0151zetes tr\u00e9ning n\u00e9lk\u00fcl. A m\u0171k\u00f6d\u00e9shez legal\u00e1bb 8 GB mem\u00f3ria m\u00e9ret\u0171 GPU sz\u00fcks\u00e9ges. el\u00e9rhet\u0151k<\/a><\/noindex> Az Impersonator m\u00f3dszer\u00e9ben a k\u00e9tdimenzi\u00f3s t\u00e9rben a test helyzet\u00e9t le\u00edr\u00f3 kulcspontokra alapozott transzform\u00e1ci\u00f3s m\u00f3dszerekt\u0151l elt\u00e9r\u0151en pr\u00f3b\u00e1lkozik egy h\u00e1romdimenzi\u00f3s h\u00e1l\u00f3 (mesh) szint\u00e9zis\u00e9vel, amely a test le\u00edr\u00e1s\u00e1ra szolg\u00e1l g\u00e9pi tanul\u00e1si m\u00f3dszerekkel.<\/p>\n

A javasolt m\u00f3dszer lehet\u0151v\u00e9 teszi a test szem\u00e9lyre szabott alakj\u00e1nak \u00e9s aktu\u00e1lis p\u00f3z\u00e1nak figyelembev\u00e9tel\u00e9vel v\u00e9gzett manipul\u00e1ci\u00f3kat, modellezve a v\u00e9gtagok term\u00e9szetes mozg\u00e1s\u00e1t.
\nA sz\u00e1rmaz\u00e1si inform\u00e1ci\u00f3, p\u00e9ld\u00e1ul text\u00far\u00e1k, st\u00edlusok, sz\u00ednek \u00e9s az arcfelismer\u00e9s meg\u0151rz\u00e9s\u00e9re a transzform\u00e1ci\u00f3s folyamat sor\u00e1n <\/p>\n

\"Megny\u00edlt<\/a><\/noindex><\/center><\/p>\n

generat\u00edv ellen\u00e1ll\u00f3 neur\u00e1lis h\u00e1l\u00f3zatot Liquid Warping GAN<\/a><\/noindex> (). A sz\u00e1rmaz\u00e1si objektum inform\u00e1ci\u00f3i \u00e9s a pontos azonos\u00edt\u00e1shoz sz\u00fcks\u00e9ges param\u00e9terek a<\/a><\/noindex>konvol\u00faci\u00f3s neur\u00e1lis h\u00e1l\u00f3 seg\u00edts\u00e9g\u00e9vel ker\u00fclnek kinyer\u00e9sre. konvol\u00faci\u00f3s neur\u00e1lis h\u00e1l\u00f3zat<\/a><\/noindex>.<\/p>\n

\r\n \"Vide\u00f3\r\n
<\/div>\r\n <\/div>
\n<\/center><\/p>\n

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

Forr\u00e1s: 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 \u0448\u0430\u043d\u0445\u0430\u0439\u0441\u043a\u043e\u0433\u043e \u0442\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u0430 \u043e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043b\u0430 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0439 Impersonator, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0438\u0439 \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 \u043c\u0435\u0442\u043e\u0434\u043e\u0432 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0441\u0438\u043c\u0443\u043b\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u044f \u043b\u044e\u0434\u0435\u0439 \u043f\u043e \u0441\u0442\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u043c \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f\u043c, \u0430 \u0442\u0430\u043a\u0436\u0435 \u0437\u0430\u043c\u0435\u043d\u044f\u0442\u044c \u043e\u0434\u0435\u0436\u0434\u0443, \u043f\u0435\u0440\u0435\u043d\u043e\u0441\u0438\u0442\u044c \u0432 \u0434\u0440\u0443\u0433\u043e\u0435 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u0438 \u043c\u0435\u043d\u044f\u0442\u044c \u0440\u0430\u043a\u0443\u0440\u0441 \u0441 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u0432\u0438\u0434\u0435\u043d \u043e\u0431\u044a\u0435\u043a\u0442. \u041a\u043e\u0434 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python \u0441 \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435\u043c \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430 PyTorch. \u0414\u043b\u044f \u0441\u0431\u043e\u0440\u043a\u0438 \u0442\u0430\u043a\u0436\u0435 \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\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\udd47Megny\u00edlt a forr\u00e1sk\u00f3d az anim\u00e1ci\u00f3s szint\u00e9zishez mesters\u00e9ges intelligencia haszn\u00e1lat\u00e1val | ProHoster","description":"A shanghai 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