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    This study introduces a novel method for creating animatable human models from images. The new approach allows for generating new views and animating any person with pose control, outperforming existing techniques.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Synthesizing novel human views and animations from limited data is challenging.
    • Existing methods often struggle to generalize to new individuals or provide user-controlled animation.
    • There is a need for a unified approach that addresses both novel view synthesis and controllable animation for multiple persons.

    Purpose of the Study:

    • To develop a generalized animatable neural human representation.
    • To enable synthesis of novel views and user-controlled animation for arbitrary persons from sparse multi-view imagery.
    • To overcome limitations of existing methods that cannot achieve both generalization and controllable animation simultaneously.

    Main Methods:

    • Learning a generalized animatable neural human representation using a sparse set of multi-view imagery.
    • Employing a 3D proxy for a shared multi-person human model.
    • Warping pose spaces to a shared canonical pose space for neural field learning.
    • Designing a neural human model with disentangled geometry and appearance to handle variations in shape, pose, and clothing.
    • Utilizing image features from spatial and surface points of the 3D proxy.

    Main Results:

    • The proposed method successfully synthesizes novel view images of arbitrary persons.
    • The learned representation allows for animating individuals with user pose control.
    • The method demonstrates superior performance compared to state-of-the-art approaches in both novel view synthesis and animation tasks.
    • The disentangled geometry and appearance contribute to handling complex human variations.

    Conclusions:

    • The developed method provides a significant advancement in creating generalized animatable neural human representations.
    • This approach effectively combines novel view synthesis and user-controlled animation capabilities for multiple persons.
    • The technique offers a robust solution for complex human modeling challenges, outperforming existing methods.