{"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\/sv\/blog\/news\/pifu-sistema-mashinnogo-obucheniya-dlya-postroeniya-3d-modeli-cheloveka-na-osnove-2d-snimkov","title":{"rendered":"PIFu \u2014 ett maskininl\u00e4rningssystem f\u00f6r att skapa en 3D-modell av en person baserat p\u00e5 2D-bilder","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

En grupp forskare fr\u00e5n flera amerikanska universitet har publicerat ett projekt PIFu<\/a><\/noindex> (Pixel-Aligned Implicit Function), som m\u00f6jligg\u00f6r till\u00e4mpningen av maskininl\u00e4rningsmetoder f\u00f6r att skapa en 3D-modell av en m\u00e4nniska baserat p\u00e5 en eller flera 2D-bilder. Systemet kan \u00e5terskapa komplexa kl\u00e4desvarianter, s\u00e5som plisserade kjolar och h\u00f6gklackade skor, samt olika frisyrer, genom att sj\u00e4lvst\u00e4ndigt \u00e5terst\u00e4lla textur och form i omr\u00e5den som \u00e4r osynliga i den projektion som anv\u00e4nds f\u00f6r att skapa 3D-modellen. F\u00f6r att \u00f6ka kvaliteten och detaljniv\u00e5n i den slutgiltiga 3D-modellen kan flera bilder tas fr\u00e5n olika vinklar. sprids<\/a><\/noindex> licensieras under MIT.<\/p>\n

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

Som k\u00e4lla f\u00f6r rekonstruktion av den volymetriska modellen anv\u00e4nds ett neuralt n\u00e4tverk som kan v\u00e4lja den mest sannolika formen och fylla i dolda element baserat p\u00e5 en modell som har tr\u00e4nats p\u00e5 olika versioner av existerande objekt. Samtidigt tillhandah\u00e5ller projektet en algoritm f\u00f6r att matcha den erh\u00e5llna volymetriska modellen med texturer fr\u00e5n de tillhandah\u00e5llna 2D-bilderna, vilket justerar pixelarna i 2D-bilden enligt deras position p\u00e5 3D-objektet och genererar de mest sannolika saknade texturerna. Valfri konvolutionsn\u00e4tverksstruktur<\/a><\/noindex>, f\u00f6r
\n\u0440\u0435\u043a\u043e\u043d\u0441\u0442\u0440\u0443\u043a\u0446\u0438\u0438 \u043f\u043e\u0432\u0435\u0440\u0445\u043d\u043e\u0441\u0442\u0438 \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0430 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0430 «Stacked hourglass<\/a><\/noindex>«, \u0430
\n f\u00f6r matchning av texturer anv\u00e4nds ett neuralt n\u00e4tverk baserat p\u00e5 arkitekturen CycleGAN<\/a><\/noindex>.<\/p>\n

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

Den f\u00e4rdigtr\u00e4nade modellen som forskarna anv\u00e4nde \u00e4r tillg\u00e4ngligt<\/a><\/noindex> \u00e4r fritt tillg\u00e4nglig f\u00f6r nedladdning, men de ursprungliga data som tr\u00e4ningen baserades p\u00e5 f\u00f6rblir st\u00e4ngda, eftersom de grundas p\u00e5 resultat fr\u00e5n kommersiell 3D-skanning. Som k\u00e4lla f\u00f6r sj\u00e4lvst\u00e4ndig tr\u00e4ning av modellen kan man anv\u00e4nda en databas med 3D-modeller<\/a><\/noindex> av m\u00e4nniskor fr\u00e5n projektet Renderpeople.<\/p>\n

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

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

K\u00e4lla: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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