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ConsistentID: Generación de retratos con preservación de identidad multimodal de grano fino

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    ConsistentID mejora la generación facial personalizada al mejorar la consistencia de la identidad a través de indicaciones multimodales de grano fino y una red de preservación de identidad. Este método logra resultados de alta fidelidad con rápidas velocidades de inferencia.

    Palabras clave:
    generación de retratospreservación de identidadmultimodalindicaciones de grano finored de preservación de identidadalta fidelidadvisión por computadorainteligencia artificialgeneración de imágenes

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    Área de la Ciencia:

    • Visión por Computadora
    • Inteligencia Artificial
    • Generación de Imágenes

    Sus antecedentes:

    • Las tecnologías basadas en difusión sobresalen en la generación facial personalizada.
    • Los métodos existentes carecen de consistencia de identidad de alta fidelidad debido al control de grano fino limitado y las estrategias integrales de preservación de identidad.

    Objetivo del estudio:

    • Introducir ConsistentID, un método novedoso para la generación diversa de retratos que preservan la identidad utilizando imágenes de referencia únicas e indicaciones multimodales de grano fino.
    • Abordar las limitaciones en el control de áreas faciales específicas y la preservación de detalles intrincados y la estructura facial general para la consistencia de la identidad.

    Principales métodos:

    • ConsistentID utiliza un generador multimodal de indicaciones faciales y una red de preservación de identidad.
    • El generador de indicaciones integra características faciales localizadas y descripciones detalladas para una reconstrucción precisa.
    • La red de preservación de identidad emplea una estrategia de localización de atención facial para una identidad consistente en todas las regiones.

    Principales resultados:

    • ConsistentID mejora significativamente la precisión de la preservación de la identidad al aprovechar la información de identidad multimodal de grano fino.
    • El conjunto de datos FGID propuesto, con más de 500 000 imágenes, mejora la diversidad y la exhaustividad del entrenamiento.
    • Los experimentos muestran que ConsistentID supera a los métodos existentes en precisión y diversidad en el conjunto de datos MyStyle, manteniendo una rápida inferencia.

    Conclusiones:

    • ConsistentID ofrece un enfoque superior para la generación de retratos que preservan la identidad, destacando en fidelidad y diversidad.
    • El método equilibra eficazmente el control facial detallado con la consistencia general de la identidad.
    • ConsistentID representa un avance significativo en la tecnología de generación de imágenes personalizadas.