{"id":102454,"date":"2021-12-03T15:36:38","date_gmt":"2021-12-03T13:36:42","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij"},"modified":"2021-12-03T15:36:38","modified_gmt":"2021-12-03T13:36:42","slug":"hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij","status":"publish","type":"post","link":"https:\/\/prohoster.info\/es\/blog\/news\/hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij","title":{"rendered":"HyperStyle \u2014 adaptaci\u00f3n del sistema de aprendizaje autom\u00e1tico StyleGAN para la edici\u00f3n de im\u00e1genes","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Un grupo de investigadores de la Universidad de Tel Aviv present\u00f3 HyperStyle, una variante invertida del sistema de aprendizaje autom\u00e1tico StyleGAN2 desarrollado por NVIDIA, que ha sido reestructurada para recrear partes faltantes durante la edici\u00f3n de im\u00e1genes reales. El c\u00f3digo est\u00e1 escrito en Python utilizando el marco PyTorch y se distribuye bajo la licencia MIT. <\/p>\n

Si StyleGAN permite sintetizar caras humanas que parecen realistas, ajustando par\u00e1metros como edad, sexo, longitud del cabello, caracter\u00edsticas de la sonrisa, forma de la nariz, color de piel, gafas y \u00e1ngulo de la fotograf\u00eda, HyperStyle permite modificar par\u00e1metros similares en fotograf\u00edas existentes, sin cambiar las caracter\u00edsticas distintivas y manteniendo el reconocimiento de la cara original. Por ejemplo, con HyperStyle se puede simular el envejecimiento de una persona en una fotograf\u00eda, cambiar el peinado, agregar gafas, barba o bigote, dar a la imagen un aspecto de personaje de caricatura o pintura, o hacer que la expresi\u00f3n facial sea triste o alegre. Adem\u00e1s, el sistema puede ser entrenado no solo para cambiar rostros humanos, sino tambi\u00e9n para cualquier objeto, como la edici\u00f3n de im\u00e1genes de autom\u00f3viles.

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

El m\u00e9todo propuesto se centra en resolver el problema de la reconstrucci\u00f3n de partes faltantes de la imagen durante la edici\u00f3n. En m\u00e9todos anteriores, el compromiso entre reconstrucci\u00f3n y editabilidad se abord\u00f3 mediante la sintonizaci\u00f3n fina del generador de im\u00e1genes para insertar partes de la imagen objetivo al recrear \u00e1reas editables que originalmente faltaban. La desventaja de estos enfoques es la necesidad de realizar un largo entrenamiento espec\u00edfico de la red neuronal para cada imagen. <\/p>\n

El m\u00e9todo basado en el algoritmo StyleGAN permite utilizar un modelo est\u00e1ndar, previamente entrenado en colecciones comunes de im\u00e1genes, para generar elementos propios de la imagen original con un nivel de fidelidad comparable a los algoritmos que requieren el entrenamiento individual de un modelo para cada imagen. Entre las ventajas de este nuevo m\u00e9todo se destaca la capacidad de modificar im\u00e1genes con un rendimiento cercano al tiempo real.

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

Se han preparado modelos entrenados para rostros de personas, autom\u00f3viles y animales, basados en colecciones como Flickr-Faces-HQ (FFHQ, 70,000 im\u00e1genes PNG de alta calidad de rostros de personas), Stanford Cars (16,000 im\u00e1genes de autom\u00f3viles) y AFHQ (fotos de animales). Adem\u00e1s, se proporciona una herramienta para entrenar sus propios modelos, as\u00ed como modelos entrenados listos para usar con codificadores y generadores est\u00e1ndar. Por ejemplo, est\u00e1n disponibles generadores para crear im\u00e1genes en el estilo Toonify, personajes de Pixar, formatear bocetos e incluso estilizar como princesas de pel\u00edculas animadas de Disney.

\"HyperStyle<\/center>
\"HyperStyle<\/center>
\"HyperStyle<\/center>
\"HyperStyle<\/center>
\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 \u0422\u0435\u043b\u044c-\u0410\u0432\u0438\u0432\u0441\u043a\u043e\u0433\u043e \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u0430 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b\u0430 HyperStyle, \u0438\u043d\u0432\u0435\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0432\u0430\u0440\u0438\u0430\u043d\u0442 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u043c\u043e\u0439 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0435\u0439 NVIDIA \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f StyleGAN2, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u0435\u0440\u0435\u0440\u0430\u0431\u043e\u0442\u0430\u043d \u0434\u043b\u044f \u0432\u043e\u0441\u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u043d\u0435\u0434\u043e\u0441\u0442\u0430\u044e\u0449\u0438\u0445 \u0447\u0430\u0441\u0442\u0435\u0439 \u043f\u0440\u0438 \u0440\u0435\u0434\u0430\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0438 \u0440\u0435\u0430\u043b\u044c\u043d\u044b\u0445 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439. \u041a\u043e\u0434 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430 PyTorch \u0438 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f \u043f\u043e\u0434 \u043b\u0438\u0446\u0435\u043d\u0437\u0438\u0435\u0439 MIT. \u0415\u0441\u043b\u0438 StyleGAN \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u0441\u0438\u043d\u0442\u0435\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0440\u0435\u0430\u043b\u0438\u0441\u0442\u0438\u0447\u043d\u043e \u0432\u044b\u0433\u043b\u044f\u0434\u044f\u0449\u0438\u0435 \u043d\u043e\u0432\u044b\u0435 \u043b\u0438\u0446\u0430 \u043b\u044e\u0434\u0435\u0439, \u0437\u0430\u0434\u0430\u0432\u0430\u044f \u0442\u0430\u043a\u0438\u0435 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b, \u043a\u0430\u043a \u0432\u043e\u0437\u0440\u0430\u0441\u0442, \u043f\u043e\u043b, 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