{"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\/pt\/blog\/news\/otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej","title":{"rendered":"C\u00f3digo aberto para s\u00edntese de anima\u00e7\u00e3o usando redes neurais","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Um grupo de pesquisadores da Universidade de Tecnologia de Xangai publicou<\/a><\/noindex> um conjunto de ferramentas Impersonator<\/a><\/noindex>, que utiliza m\u00e9todos de aprendizado de m\u00e1quina para simular movimentos de pessoas em imagens est\u00e1ticas, al\u00e9m de trocar roupas, transferir para outro ambiente e alterar o \u00e2ngulo de vis\u00e3o do objeto. O c\u00f3digo foi escrito na linguagem Python
\nusando o framework PyTorch<\/a><\/noindex>. Para a montagem, \u00e9 necess\u00e1rio tamb\u00e9m torchvision<\/a><\/noindex> e CUDA Toolkit. <\/p>\n

\"C\u00f3digo<\/center><\/p>\n

A ferramenta recebe uma imagem bidimensional e sintetiza um resultado alterado com base no modelo escolhido. Suporta tr\u00eas tipos de transforma\u00e7\u00f5es:
\nCria\u00e7\u00e3o de um objeto em movimento que reproduz os movimentos para os quais o modelo foi treinado. Transfer\u00eancia de elementos de apar\u00eancia do modelo para o objeto (por exemplo, mudar de roupa). Gera\u00e7\u00e3o de um novo \u00e2ngulo (por exemplo, sintetizar uma imagem de perfil com base em uma foto de frente). Todos os tr\u00eas m\u00e9todos podem ser combinados, por exemplo, \u00e9 poss\u00edvel gerar um v\u00eddeo a partir de uma foto, imitando a execu\u00e7\u00e3o de uma acrobacia complexa com roupas diferentes. <\/p>\n

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

Durante o processo de s\u00edntese, opera\u00e7\u00f5es de segmenta\u00e7\u00e3o do objeto na foto e forma\u00e7\u00e3o dos elementos de fundo que faltam durante a movimenta\u00e7\u00e3o s\u00e3o realizadas. O modelo para a rede neural pode ser treinado uma vez e utilizado para v\u00e1rias transforma\u00e7\u00f5es. Para download est\u00e3o dispon\u00edveis<\/a><\/noindex> modelos prontos, permitindo o uso imediato da ferramenta sem necessidade de treinamento pr\u00e9vio. Para opera\u00e7\u00e3o, \u00e9 necess\u00e1rio um GPU com pelo menos 8GB de mem\u00f3ria.<\/p>\n

Ao contr\u00e1rio dos m\u00e9todos de transforma\u00e7\u00e3o baseados em pontos chave que descrevem a posi\u00e7\u00e3o do corpo no espa\u00e7o bidimensional, no Impersonator foi tentada a s\u00edntese de uma malha tridimensional (mesh) com a descri\u00e7\u00e3o do corpo, utilizando m\u00e9todos de aprendizado de m\u00e1quina.
\nO m\u00e9todo proposto permite realizar manipula\u00e7\u00f5es considerando a forma personalizada do corpo e a pose atual, modelando movimentos naturais dos membros. <\/p>\n

\"C\u00f3digo<\/a><\/noindex><\/center><\/p>\n

Para preservar informa\u00e7\u00f5es originais, como texturas, estilo, cores e reconhecimento facial, durante a transforma\u00e7\u00e3o aplica-se uma rede neural generativa adversarial<\/a><\/noindex> (Liquid Warping GAN<\/a><\/noindex>). A informa\u00e7\u00e3o sobre o objeto original e par\u00e2metros para sua identifica\u00e7\u00e3o precisa s\u00e3o extra\u00eddos por meio da aplica\u00e7\u00e3o de uma rede neural convolucional<\/a><\/noindex>.<\/p>\n

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

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

Fonte: 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\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 aberto para s\u00edntese de anima\u00e7\u00e3o usando redes neurais | ProHoster","description":"Um grupo de pesquisadores da Universidade de Tecnologia de Xangai 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