{"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\/it\/blog\/news\/pifu-sistema-mashinnogo-obucheniya-dlya-postroeniya-3d-modeli-cheloveka-na-osnove-2d-snimkov","title":{"rendered":"PIFu \u2014 un sistema di apprendimento automatico per la creazione di modelli 3D di persona basati su immagini 2D","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Un gruppo di ricercatori di diverse universit\u00e0 americane ha pubblicato un progetto PIFu<\/a><\/noindex> (Pixel-Aligned Implicit Function), che consente di applicare metodi di apprendimento automatico per creare un modello 3D di una persona a partire da una o pi\u00f9 immagini bidimensionali. Il sistema permette di ricreare varianti complesse di abbigliamento, come gonne plissettate e scarpe con il tacco, e diverse acconciature, ripristinando autonomamente la texture e la forma nelle aree invisibili nella proiezione da cui viene creato il modello 3D. Per aumentare la qualit\u00e0 e la definizione del modello 3D finale, possono essere utilizzate pi\u00f9 immagini da diverse angolazioni. Il codice del progetto \u00e8 scritto in linguaggio Python utilizzando il framework PyTorch e \u00e8 distribuito<\/a><\/noindex> sotto licenza MIT.<\/p>\n

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

Come fonte per la ricostruzione del modello volumetrico, viene utilizzata una rete neurale che permette di scegliere la forma pi\u00f9 probabile e dedurre elementi nascosti, basandosi su un modello addestrato su varie versioni di oggetti esistenti. Parallelamente, il progetto fornisce un algoritmo per il confronto del modello volumetrico ottenuto con le texture delle immagini bidimensionali fornite, che allinea i pixel dell'immagine 2D in base alla loro posizione sul modello 3D e genera le texture mancanti pi\u00f9 probabili. Per codificare l'immagine, pu\u00f2 essere utilizzata qualsiasi rete neurale convoluzionale<\/a><\/noindex>, per
\nper la ricostruzione della superficie \u00e8 stata applicata l'architettura «Stacked hourglass<\/a><\/noindex>«, a
\n per il confronto delle texture \u00e8 stata utilizzata una rete neurale basata sull'architettura CycleGAN<\/a><\/noindex>.<\/p>\n

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

Il modello pre-addestrato utilizzato dai ricercatori disponibile<\/a><\/noindex> \u00e8 disponibile per il download libero, ma i dati originali su cui \u00e8 stato effettuato l'addestramento rimangono chiusi, poich\u00e9 si basano su risultati di scansione 3D commerciali. Come fonte per l'addestramento autonomo del modello, pu\u00f2 essere utilizzato un database di modelli 3D<\/a><\/noindex> di persone del progetto Renderpeople.<\/p>\n

\r\n \"Guarda\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":"

\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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