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SeparateGen: Generación de Personajes 3D Basada en Componentes Semánticos a partir de Imágenes Únicas

Dong-Yang Li, Yi-Long Liu, Zi-Xian Liu

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    SeparateGen crea personajes 3D de alta calidad a partir de imágenes únicas separando componentes semánticos. Este enfoque novedoso resuelve el enredo de mallas, permitiendo un rigging y animación más sencillos para activos 3D.

    Palabras clave:
    Generación de personajes 3DImágenes únicasComponentes semánticosEnredo de mallasRiggingAnimaciónVisión por computadoraGráficos por computadoraInteligencia artificial

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    Área de la Ciencia:

    • Visión por computadora
    • Gráficos por computadora
    • Inteligencia artificial

    Sus antecedentes:

    • La generación de personajes 3D detallados a partir de imágenes únicas es difícil debido al enredo de componentes semánticos.
    • Los métodos existentes a menudo dan como resultado una topología de malla deficiente, lo que limita su usabilidad en animación y rigging.

    Objetivo del estudio:

    • Introducir SeparateGen, un marco para la generación de personajes 3D de alta calidad a partir de imágenes únicas.
    • Reconstruir explícitamente los personajes en distintos componentes semánticos (cuerpo, ropa, cabello, zapatos).

    Principales métodos:

    • Utilizar un modelo de difusión multi-vista para la generación de imágenes consistentes en pose canónica A.
    • Emplear un novedoso modelo de reconstrucción consciente de componentes (SC-LRM) para la descomposición y reconstrucción de componentes de alta fidelidad.
    • Introducir SC-Anime, un conjunto de datos a gran escala de 7580 personajes 3D de estilo anime con anotaciones a nivel de componente.

    Principales resultados:

    • SeparateGen supera significativamente los métodos de última generación en calidad de reconstrucción y consistencia multi-vista.
    • El enfoque basado en componentes resuelve eficazmente los problemas de enredo de mallas.
    • Los personajes generados son adecuados para rigging y reutilización de activos sin problemas.

    Conclusiones:

    • SeparateGen avanza en la generación de personajes 3D listos para aplicaciones a partir de imágenes únicas.
    • El conjunto de datos SC-Anime y el código se publicarán para facilitar la investigación futura.