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title
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updated
2026-02-25 18:02:20
raw text
Projectpage of Animatable Gaussians Animatable Gaussians: Learning Pose-dependent Gaussian Maps for High-fidelity Human Avatar Modeling CVPR 2024 Zhe Li 1 , Zerong Zheng 2 , Lizhen Wang 1 , Yebin Liu 1 1 Tsinghua University 2 NNKosmos Technology Paper Video Code Extension for Relighting Abstract Modeling animatable human avatars from RGB videos is a long-standing and challenging problem. Recent works usually adopt MLP-based neural radiance fields (NeRF) to represent 3D humans, but it remains difficult for pure MLPs to regress pose-dependent garment details. To this end, we introduce Animatable Gaussians, a new avatar representation that leverages powerful 2D CNNs and 3D Gaussian splatting to create high-fidelity avatars. To associate 3D Gaussians with the animatable avatar, we learn a parametric template from the input videos, and then parameterize the template on two front & back canonical Gaussian maps where each pixel represents a 3D Gaussian. ...
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2026-02-25 18:02:20
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