{"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\/et\/blog\/news\/otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej","title":{"rendered":"Code for animation synthesis using neural networks is now open","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Shangha\u00ef tehnikak\u00f5rgkooli teadlaste grupp avalikustas<\/a><\/noindex> t\u00f6\u00f6riistakomplekt Impersonator<\/a><\/noindex>, mis v\u00f5imaldab masin\u00f5ppe meetodite abil simuleerida inimeste liikumisi staatilistel piltidel, samuti asendada riideid, viia objekt teise keskkonda ja muuta vaatenurka, mille alt objekt on n\u00e4htav. Kood on kirjutatud keeles Python
\nrakendades raamistikku PyTorch<\/a><\/noindex>. Kogumise jaoks on samuti vajalik torchvision<\/a><\/noindex> ja CUDA Toolkit. <\/p>\n

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

T\u00f6\u00f6riist v\u00f5tab sisendina kahem\u00f5\u00f5tmelise pildi ja s\u00fcnteesib muudetud tulemuse valitud mudeli p\u00f5hjal. Toe all on kolm varianti transformatsioonidest:
\nLiikuva objekti loomine, mis kordab mudeleid, millele ta on koolitatud. V\u00e4limuse elementide \u00fclekandmine modellist objektile (nt riiete vahetamine). Uue vaatenurga genereerimine (nt portree s\u00fcnteesimine p\u00f5hjal fotost). K\u00f5iki kolme meetodit saab kombineerida, n\u00e4iteks on v\u00f5imalik fotost genereerida video, mis imiteerib keerulise akrobaatikatriki sooritamist teistes riietes. <\/p>\n

\r\n \"M\u00e4ngi\r\n
<\/div>\r\n <\/div><\/center><\/p>\n

S\u00fcnteesi k\u00e4igus tehakse samal ajal ka objektide eristamise ja tausta puuduvate elementide loomise toimingud liikumise ajal. Mudelit n\u00e4rviv\u00f5rgu jaoks saab koolitada \u00fche korra ja kasutada erinevateks transformatsioonideks. Laadimiseks saadaval<\/a><\/noindex> valmis mudelid, mis v\u00f5imaldavad t\u00f6\u00f6riista kohe kasutada ilma eelneva koolituseta. T\u00f6\u00f6ks on vajalik GPU, mille m\u00e4lu suurus on v\u00e4hemalt 8GB.<\/p>\n

Erinevalt transformatsioonimeetoditest, mis p\u00f5hinevad v\u00f5tmepunktide transformatsioonil, mis kirjeldavad keha asukohta kahem\u00f5\u00f5tmelises ruumis, on Impersonatoris p\u00fc\u00fctud s\u00fcnteesida kolme m\u00f5\u00f5tmelist v\u00f5rku (mesh), mis kirjeldab keha, kasutades masin\u00f5ppe meetodeid.
\nPakutud meetod v\u00f5imaldab manipuleerida, arvestades isikup\u00e4rastatud keha kuju ja hetki, modelleerides loomulikke j\u00e4semete liikumisi. <\/p>\n

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

Algteabe, n\u00e4iteks tekstuuride, stiili, v\u00e4rvide ja n\u00e4o \u00e4ratuntavuse s\u00e4ilitamiseks rakendatakse transformatsiooni k\u00e4igus generatiivset vastandv\u00f5rku<\/a><\/noindex> (Liquid Warping GAN<\/a><\/noindex>). Algobjekti teave ja parameetrid selle t\u00e4pseks tuvastamiseks ekstraheeritakse konvolutsiooniliste n\u00e4rviv\u00f5rkude rakendamise teel konvolutsiooniline n\u00e4rviv\u00f5rk<\/a><\/noindex>.<\/p>\n

\r\n \"M\u00e4ngi\r\n
<\/div>\r\n <\/div>
\n<\/center><\/p>\n

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

Allikas: 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\udd47Ava kood animatsiooni s\u00fcnteesimiseks n\u00e4rviv\u00f5rkude abil | ProHoster","description":"Shanghai Tehnika \u00dclikooli teadlaste r\u00fchm avaldas 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