{"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\/pt\/blog\/news\/pifu-sistema-mashinnogo-obucheniya-dlya-postroeniya-3d-modeli-cheloveka-na-osnove-2d-snimkov","title":{"rendered":"PIFu \u2014 um sistema de aprendizado de m\u00e1quina para a cria\u00e7\u00e3o de um modelo 3D de uma pessoa com base em imagens 2D.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Um grupo de pesquisadores de v\u00e1rias universidades americanas publicou um projeto PIFu<\/a><\/noindex> (Fun\u00e7\u00e3o Impl\u00edcita Alinhada por Pixels), que permite aplicar m\u00e9todos de aprendizado de m\u00e1quina para construir um modelo 3D de uma pessoa a partir de uma ou v\u00e1rias imagens bidimensionais. O sistema possibilita a recria\u00e7\u00e3o de varia\u00e7\u00f5es complexas de roupas, como saias com pregas e sapatos de salto alto, al\u00e9m de diversos penteados, restaurando automaticamente textura e forma em \u00e1reas invis\u00edveis na proje\u00e7\u00e3o, pela qual o modelo 3D \u00e9 constru\u00eddo. Para aumentar a qualidade e o detalhamento do modelo 3D final, v\u00e1rias imagens de diferentes \u00e2ngulos podem ser utilizadas. O c\u00f3digo do projeto est\u00e1 escrito na linguagem Python utilizando o framework PyTorch e \u00e9 distribu\u00eddo<\/a><\/noindex> sob a licen\u00e7a MIT.<\/p>\n

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

Como fonte para a reconstru\u00e7\u00e3o do modelo volum\u00e9trico, uma rede neural \u00e9 utilizada para escolher a forma mais prov\u00e1vel e preencher elementos ocultos, baseando-se em um modelo treinado com v\u00e1rias vers\u00f5es de objetos existentes. Paralelamente, o projeto fornece um algoritmo para mapear o modelo volum\u00e9trico obtido com as texturas nas imagens bidimensionais fornecidas, que alinha os pixels da imagem 2D de acordo com sua posi\u00e7\u00e3o no objeto 3D e gera as texturas ausentes mais prov\u00e1veis. Para codifica\u00e7\u00e3o da imagem, qualquer rede neural convolucional<\/a><\/noindex>, para
\na arquitetura foi aplicada \u00e0 reconstru\u00e7\u00e3o da superf\u00edcie «Hourglass empilhado<\/a><\/noindex>«, a
\n para mapeamento de texturas, foi utilizada uma rede neural baseada na arquitetura CycleGAN<\/a><\/noindex>.<\/p>\n

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

O modelo treinado utilizado pelos pesquisadores est\u00e1 dispon\u00edvel<\/a><\/noindex> est\u00e1 dispon\u00edvel para download gratuito, mas os dados originais nos quais o treinamento foi realizado permanecem fechados, pois s\u00e3o baseados nos resultados de escaneamento 3D comercial. Como fonte para o auto-treinamento do modelo, pode ser utilizada uma base de modelos 3D<\/a><\/noindex> de pessoas do projeto Renderpeople.<\/p>\n

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