{"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\/pl\/blog\/news\/otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej","title":{"rendered":"Otworzono kod do syntezowania animacji za pomoc\u0105 sieci neuronowych","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Grupa badawcza z Uniwersytetu Technicznego w Szanghaju opublikowa\u0142a<\/a><\/noindex> narz\u0119dzia Impersonator<\/a><\/noindex>, umo\u017cliwiaj\u0105cy dzi\u0119ki metodom uczenia maszynowego symulowanie ruch\u00f3w ludzi na statycznych obrazach, a tak\u017ce wymian\u0119 ubra\u0144, przenoszenie do innego otoczenia oraz zmian\u0119 k\u0105ta widzenia obiektu. Kod napisany jest w j\u0119zyku Python
\nz wykorzystaniem frameworka PyTorch<\/a><\/noindex>. Wymagana jest tak\u017ce torchvision<\/a><\/noindex> i CUDA Toolkit. <\/p>\n

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

Narz\u0119dzie otrzymuje dwuwymiarowy obraz i syntetyzuje zmieniony wynik na podstawie wybranego modelu. Obs\u0142ugiwane s\u0105 trzy warianty transformacji:
\nTworzenie ruchomego obiektu, na\u015bladuj\u0105cego ruchy, na kt\u00f3rych model zosta\u0142 wytrenowany. Przeniesienie element\u00f3w wygl\u0105du z modelu do obiektu (na przyk\u0142ad zmiana ubrania). Generacja nowego k\u0105ta widzenia (na przyk\u0142ad syntetyzowanie obrazu z profilu na podstawie zdj\u0119cia frontalnego). Wszystkie trzy metody mog\u0105 by\u0107 \u0142\u0105czone, na przyk\u0142ad mo\u017cna generowa\u0107 wideo z zdj\u0119cia, imituj\u0105ce wykonanie skomplikowanego akrobatycznego triku w innych ubraniach. <\/p>\n

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

Podczas syntezowania przeprowadzane s\u0105 r\u00f3wnie\u017c operacje wyodr\u0119bniania obiektu na zdj\u0119ciu oraz formowania brakuj\u0105cych element\u00f3w t\u0142a przy przenoszeniu. Model dla sieci neuronowej mo\u017ce by\u0107 wytrenowany raz i u\u017cywany do r\u00f3\u017cnych transformacji. Aby pobra\u0107 dost\u0119pne s\u0105<\/a><\/noindex> gotowe modele, kt\u00f3re umo\u017cliwiaj\u0105 natychmiastowe wykorzystanie narz\u0119dzia bez przeprowadzania wst\u0119pnego treningu. Wymagany jest GPU o pami\u0119ci co najmniej 8GB.<\/p>\n

W przeciwie\u0144stwie do metod transformacji opartych na przekszta\u0142ceniach za pomoc\u0105 punkt\u00f3w kluczowych opisuj\u0105cych pozycj\u0119 cia\u0142a w przestrzeni dwuwymiarowej, w Impersonator podj\u0119to pr\u00f3b\u0119 syntezy tr\u00f3jwymiarowej siatki (mesh) z opisem cia\u0142a, wykorzystuj\u0105c metody uczenia maszynowego.
\nProponowana metoda pozwala na przeprowadzanie manipulacji z uwzgl\u0119dnieniem zindywidualizowanej formy cia\u0142a i aktualnej pozy, modeluj\u0105c naturalne ruchy ko\u0144czyn. <\/p>\n

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

Aby zachowa\u0107 informacje o pierwotnych elementach, takich jak tekstury, styl, kolory i rozpoznawalno\u015b\u0107 twarzy, podczas transformacji zastosowano generatywn\u0105 sie\u0107 neuronow\u0105<\/a><\/noindex> (Liquid Warping GAN<\/a><\/noindex>). Informacje o oryginalnym obiekcie oraz parametry dla jego dok\u0142adnej identyfikacji s\u0105 wyodr\u0119bniane poprzez zastosowanie splotowej sieci neuronowej<\/a><\/noindex>.<\/p>\n

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

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

\u0179r\u00f3d\u0142o: 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\udd47Udost\u0119pniono kod do syntezowania animacji za pomoc\u0105 sieci neuronowych | ProHoster","description":"Grupa badaczy z Uniwersytetu Technologicznego w Szanghaju opublikowa\u0142a 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