FFAvatar: Feed-Forward 4D Head Avatar Reconstruction from Sparse Portrait Images
We present FFAvatar, a Transformer-based 3D Gaussian framework for fast construction of high-quality and animatable 4D head avatars from one or more reference portrait images. Unlike existing feed-forward approaches that require a fixed number of input views, FFAvatar supports incremental reconstruction, progressively refining the avatar representation as additional reference images become available. At the core of our method is an alternating attention mechanism that disentangles identity appearance from expression and viewpoint variations, enabling the reconstruction of a canonical 3D appearance that remains consistent across poses and facial expressions. To balance visual fidelity and ...
| Authors | Jianjiang Yao, Ke Xian, Renxiang Dai et al. |
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| Primary category | cs.CV |
| Published | 29 June 2026 |
| Last updated at source | 14 July 2026 |
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