{"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\/es\/blog\/news\/otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej","title":{"rendered":"Se ha abierto el c\u00f3digo para la s\u00edntesis de animaciones utilizando redes neuronales","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Un grupo de investigadores de la Universidad T\u00e9cnica de Shangh\u00e1i ha publicado<\/a><\/noindex> un conjunto de herramientas Impersonator<\/a><\/noindex>, que permite simular movimientos de personas en im\u00e1genes est\u00e1ticas mediante m\u00e9todos de aprendizaje autom\u00e1tico, as\u00ed como cambiar la ropa, trasladar a otro entorno y cambiar el \u00e1ngulo desde el cual se ve el objeto. El c\u00f3digo est\u00e1 escrito en Python
\nutilizando el marco PyTorch<\/a><\/noindex>. Para la compilaci\u00f3n tambi\u00e9n se requiere torchvision<\/a><\/noindex> y CUDA Toolkit. <\/p>\n

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

El conjunto de herramientas recibe como entrada una imagen bidimensional y sintetiza un resultado modificado basado en el modelo seleccionado. Hay tres tipos de transformaciones soportadas:
\nCreaci\u00f3n de un objeto en movimiento que replica los movimientos en los que se entren\u00f3 el modelo. Transferencia de elementos de apariencia del modelo al objeto (por ejemplo, cambio de ropa). Generaci\u00f3n de un nuevo \u00e1ngulo (por ejemplo, s\u00edntesis de una imagen de perfil basada en una foto de frente). Los tres m\u00e9todos pueden combinarse; por ejemplo, se puede generar un video a partir de una foto, imitando la realizaci\u00f3n de un truco acrob\u00e1tico complejo con ropa diferente. <\/p>\n

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

Durante el proceso de s\u00edntesis, se realizan operaciones de segmentaci\u00f3n del objeto en la fotograf\u00eda y de formaci\u00f3n de los elementos de fondo que faltan al trasladarse. El modelo para la red neuronal puede entrenarse una vez y utilizarse para diversas transformaciones. Para la carga est\u00e1n disponibles<\/a><\/noindex> modelos listos para usar, que permiten utilizar inmediatamente el conjunto de herramientas sin necesidad de un entrenamiento previo. Se requiere una GPU con al menos 8 GB de memoria.<\/p>\n

A diferencia de los m\u00e9todos de transformaci\u00f3n basados en la transformaci\u00f3n por puntos clave que describen la ubicaci\u00f3n del cuerpo en un espacio bidimensional, en Impersonator se ha intentado sintetizar una malla tridimensional (mesh) que describe el cuerpo, utilizando m\u00e9todos de aprendizaje autom\u00e1tico.
\nEl m\u00e9todo propuesto permite realizar manipulaciones teniendo en cuenta la forma personalizada del cuerpo y la postura actual, modelando movimientos naturales de las extremidades. <\/p>\n

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

Para preservar la informaci\u00f3n original, como texturas, estilo, colores y reconocimiento facial, durante el proceso de transformaci\u00f3n se aplica una red generativa antag\u00f3nica<\/a><\/noindex> (Liquid Warping GAN<\/a><\/noindex>). La informaci\u00f3n sobre el objeto original y los par\u00e1metros para su identificaci\u00f3n precisa se extraen mediante la aplicaci\u00f3n red neuronal convolucional<\/a><\/noindex>.<\/p>\n

\r\n \"Reproducir\r\n
<\/div>\r\n <\/div>
\n<\/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 \u0448\u0430\u043d\u0445\u0430\u0439\u0441\u043a\u043e\u0433\u043e \u0442\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u0430 \u043e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043b\u0430 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0439 Impersonator, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0438\u0439 \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 \u043c\u0435\u0442\u043e\u0434\u043e\u0432 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0441\u0438\u043c\u0443\u043b\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u044f \u043b\u044e\u0434\u0435\u0439 \u043f\u043e \u0441\u0442\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u043c \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f\u043c, \u0430 \u0442\u0430\u043a\u0436\u0435 \u0437\u0430\u043c\u0435\u043d\u044f\u0442\u044c \u043e\u0434\u0435\u0436\u0434\u0443, \u043f\u0435\u0440\u0435\u043d\u043e\u0441\u0438\u0442\u044c \u0432 \u0434\u0440\u0443\u0433\u043e\u0435 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u0438 \u043c\u0435\u043d\u044f\u0442\u044c \u0440\u0430\u043a\u0443\u0440\u0441 \u0441 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u0432\u0438\u0434\u0435\u043d \u043e\u0431\u044a\u0435\u043a\u0442. \u041a\u043e\u0434 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python \u0441 \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435\u043c \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430 PyTorch. \u0414\u043b\u044f \u0441\u0431\u043e\u0440\u043a\u0438 \u0442\u0430\u043a\u0436\u0435 \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\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\udd47C\u00f3digo abierto para la s\u00edntesis de animaciones mediante redes neuronales | ProHoster","description":"Un grupo de investigadores de la Universidad T\u00e9cnica de 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