{"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\/es\/blog\/news\/pifu-sistema-mashinnogo-obucheniya-dlya-postroeniya-3d-modeli-cheloveka-na-osnove-2d-snimkov","title":{"rendered":"PIFu \u2014 un sistema de aprendizaje autom\u00e1tico para construir un modelo 3D de una persona basado en im\u00e1genes 2D","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Un grupo de investigadores de varias universidades estadounidenses ha publicado un proyecto PIFu<\/a><\/noindex> (Funci\u00f3n Impl\u00edcita Alineada por P\u00edxeles), que permite aplicar m\u00e9todos de aprendizaje autom\u00e1tico para construir un modelo 3D de una persona a partir de una o varias im\u00e1genes bidimensionales. El sistema permite recrear variantes complejas de ropa, como faldas con pliegues y zapatos de tac\u00f3n, y diversos peinados, restaurando por s\u00ed mismo la textura y la forma en \u00e1reas que son invisibles en la proyecci\u00f3n a partir de la cual se realiza la construcci\u00f3n del modelo 3D. Para aumentar la calidad y el detalle del modelo 3D final, se pueden utilizar varias im\u00e1genes desde diferentes \u00e1ngulos. El c\u00f3digo del proyecto est\u00e1 escrito en Python utilizando el marco PyTorch y se distribuye<\/a><\/noindex> bajo licencia MIT.<\/p>\n

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

como fuente para la reconstrucci\u00f3n del modelo volum\u00e9trico se utiliza una red neuronal que permite elegir la forma m\u00e1s probable y deducir elementos ocultos, bas\u00e1ndose en un modelo entrenado sobre diversas variantes de objetos existentes. Paralelamente, el proyecto proporciona un algoritmo para emparejar el modelo volum\u00e9trico obtenido con las texturas de las im\u00e1genes bidimensionales proporcionadas, que alinea los p\u00edxeles de la imagen 2D en funci\u00f3n de su posici\u00f3n en el objeto 3D y genera las texturas que probablemente faltan. Cualquier red neuronal convolucional<\/a><\/noindex>, para
\npara la reconstrucci\u00f3n de la superficie se aplic\u00f3 la arquitectura «Stacked hourglass<\/a><\/noindex>«, y
\n para el emparejamiento de texturas se ha utilizado una red neuronal basada en la arquitectura CycleGAN<\/a><\/noindex>.<\/p>\n

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

El modelo previamente entrenado que utilizaron los investigadores est\u00e1 disponible<\/a><\/noindex> est\u00e1 disponible para descarga gratuita, pero los datos originales en los que se llev\u00f3 a cabo el entrenamiento permanecen cerrados, ya que se basan en los resultados de un escaneo 3D comercial. Como fuente para el entrenamiento independiente del modelo puede utilizarse una base de datos de modelos 3D<\/a><\/noindex> de personas del proyecto Renderpeople.<\/p>\n

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

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

Fuente: 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 \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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