{"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\/da\/blog\/news\/pifu-sistema-mashinnogo-obucheniya-dlya-postroeniya-3d-modeli-cheloveka-na-osnove-2d-snimkov","title":{"rendered":"PIFu \u2014 et maskinl\u00e6ringssystem til at skabe en 3D-model af en person baseret p\u00e5 2D-billeder.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

En gruppe forskere fra flere amerikanske universiteter har offentliggjort et projekt PIFu<\/a><\/noindex> (Pixel-Aligned Implicit Function), der g\u00f8r det muligt at anvende metoder til maskinl\u00e6ring til at konstruere en 3D-model af en person baseret p\u00e5 et eller flere 2D-billeder. Systemet kan genskabe komplekse kl\u00e6deskabsvarianter som nederdele med folder og h\u00f8jh\u00e6lede sko samt forskellige frisurer, idet det selv kan genskabe tekstur og form i de omr\u00e5der, der er usynlige i projektet, hvormed 3D-modellen konstrueres. For at \u00f8ge kvaliteten og detaljegraden af den endelige 3D-model kan der anvendes flere billeder fra forskellige vinkler. Koden til projektet er skrevet p\u00e5 Python ved hj\u00e6lp af PyTorch-rammen. distribueres<\/a><\/noindex> under MIT-licensen.<\/p>\n

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

Som kilde til rekonstruering af den rummelige model anvendes et neuralt netv\u00e6rk, der kan v\u00e6lge den mest sandsynlige form og udfylde skjulte elementer baseret p\u00e5 en model, der er tr\u00e6net p\u00e5 forskellige varianter af eksisterende objekter. Samtidig giver projektet en algoritme til at matche den opn\u00e5ede rummelige model med teksturerne i de leverede 2D-billeder, som justerer pixels fra 2D-billedet i henhold til deres position p\u00e5 3D-objektet og genererer de mest sandsynlige manglende teksturer. Enhver konvolutionelt neuralt netv\u00e6rk<\/a><\/noindex>, for
\noverfladefornyelsen anvendte arkitekturen «Stacked hourglass<\/a><\/noindex>«, og
\n til teksturmatchning anvendes et neuralt netv\u00e6rk baseret p\u00e5 arkitekturen CycleGAN<\/a><\/noindex>.<\/p>\n

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

Den f\u00e6rdige model, der blev anvendt af forskerne, tilg\u00e6ngelig<\/a><\/noindex> er tilg\u00e6ngelig til gratis download, men de oprindelige data, som tr\u00e6ningen blev udf\u00f8rt p\u00e5, forbliver lukkede, da de er baseret p\u00e5 resultaterne af kommerciel 3D-scanning. Som kilde til selvtr\u00e6ning af modellen kan der anvendes en database af 3D-modeller<\/a><\/noindex> af mennesker fra projektet Renderpeople.<\/p>\n

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

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

Kilde: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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