3D Gaussian Blendshapes for Head Avatar Animation
Shengjie Ma, Yanlin Weng, Tianjia Shao, Kun Zhou
摘要
We introduce 3D Gaussian blendshapes for modeling photorealistic head avatars. Taking a monocular video as input, we learn a base head model of neutral expression, along with a group of expression blendshapes, each of which corresponds to a basis expression in classical parametric face models. Both the neutral model and expression blendshapes are represented as 3D Gaussians, which contain a few properties to depict the avatar appearance. The avatar model of an arbitrary expression can be effectively generated by combining the neutral model and expression blendshapes through linear blending of Gaussians with the expression coefficients. High-fidelity head avatar animations can be synthesized in real time using Gaussian splatting. Compared to state-of-the-art methods, our Gaussian blendshape representation better captures high-frequency details exhibited in input video, and achieves superior rendering performance.
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引用它的顶会 Paper27
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- BecomingLit: Relightable Gaussian Avatars with Hybrid Neural ShadingJonathan Schmidt, Simon Giebenhain, Matthias NießnerNeurIPS 2025 · 被引用 9 次
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- STAvatar: Soft Binding and Temporal Density Control for Monocular 3D Head Avatars ReconstructionJiankuo Zhao, Xiangyu Zhu, Zidu Wang, Zhen LeiCVPR 2026 · 被引用 4 次
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- HeadNeRF: A Realtime NeRF-based Parametric Head ModelYang Hong, Bo Peng, Haiyao Xiao, Ligang Liu 等CVPR 2022 · 被引用 189 次
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