{"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\/ro\/blog\/news\/pifu-sistema-mashinnogo-obucheniya-dlya-postroeniya-3d-modeli-cheloveka-na-osnove-2d-snimkov","title":{"rendered":"PIFu \u2014 un sistem de \u00eenv\u0103\u021bare automat\u0103 pentru construirea unui model 3D al unei persoane pe baza unor imagini 2D","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Un grup de cercet\u0103tori de la mai multe universit\u0103\u021bi americane a publicat un proiect PIFu<\/a><\/noindex> (Func\u021bia Implicit Aliniat\u0103 pe Pixel), care permite aplicarea metodelor de \u00eenv\u0103\u021bare automat\u0103 pentru a construi un model 3D al unei persoane pe baza uneia sau mai multor imagini bidimensionale. Sistemul permite recrearea variantelor complexe de \u00eembr\u0103c\u0103minte, precum fuste plisate \u0219i pantofi cu toc, precum \u0219i diferite coafuri, ref\u0103c\u00e2nd singur textura \u0219i forma \u00een regiunile invizibile \u00een proiec\u021bia din care se realizeaz\u0103 construc\u021bia modelului 3D. Pentru a cre\u0219te calitatea \u0219i detalierea modelului 3D final, pot fi utilizate mai multe imagini din perspective diferite. Codul proiectului este scris \u00een limbajul Python folosind framework-ul PyTorch \u0219i se r\u0103sp\u00e2nde\u0219te<\/a><\/noindex> sub licen\u021ba MIT.<\/p>\n

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

Ca surs\u0103 pentru reconstruc\u021bia modelului volumetric se folose\u0219te o re\u021bea neuronal\u0103 care permite alegerea celei mai probabile forme \u0219i completarea elementelor ascunse, baz\u00e2ndu-se pe un model antrenat pe diferite variante ale obiectelor existente. \u00cen acela\u0219i timp, proiectul ofer\u0103 un algoritm pentru corelarea modelului volumetric ob\u021binut cu texturile din imaginile bidimensionale furnizate, care aliniaz\u0103 pixele imaginii 2D conform pozi\u021biei lor pe obiectul 3D \u0219i genereaz\u0103 cele mai probabile texturi lips\u0103. Pentru codificarea imaginii poate fi utilizat\u0103 orice re\u021bea neuronal\u0103 convolu\u021bional\u0103<\/a><\/noindex>, pentru
\nreconstruc\u021biei suprafe\u021bei a fost aplicat\u0103 arhitectura «Stacked hourglass<\/a><\/noindex>«, iar
\n pentru corelarea texturilor a fost implicat\u0103 o re\u021bea neuronal\u0103 bazat\u0103 pe arhitectura CycleGAN<\/a><\/noindex>.<\/p>\n

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

Modelul antrenat utilizat de cercet\u0103tori este disponibil\u0103<\/a><\/noindex> disponibil pentru desc\u0103rcare gratuit\u0103, dar datele originale pe care s-a efectuat antrenamentul r\u0103m\u00e2n \u00eenchise, deoarece se bazeaz\u0103 pe rezultatele scan\u0103rii 3D comerciale. Ca surs\u0103 pentru auto-antrenarea modelului poate fi folosit\u0103 o baz\u0103 de date 3D cu modele<\/a><\/noindex> de oameni de la proiectul Renderpeople.<\/p>\n

\r\n \"Reda\u021bi\r\n
<\/div>\r\n <\/div><\/center><\/p>\n

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

Sursa: opennet.ro<\/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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