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HyperGaussians improve the creation of high-fidelity animatable face avatars from monocular videos by using a novel representation that combines neural implicit surfaces with Gaussian functions. This approach allows for capturing detailed facial geometry and expressions with high accuracy. The method efficiently models complex facial features and dynamics, enabling the generation of realistic and animatable 3D face avatars from single-view video inputs.

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