Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.13837 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917234311430144 |
|---|---|
| author | Ji, Xinya Weiss, Sebastian Kansy, Manuel Naruniec, Jacek Cao, Xun Solenthaler, Barbara Bradley, Derek |
| author_facet | Ji, Xinya Weiss, Sebastian Kansy, Manuel Naruniec, Jacek Cao, Xun Solenthaler, Barbara Bradley, Derek |
| contents | Despite recent progress in 3D Gaussian-based head avatar modeling, efficiently generating high fidelity avatars remains a challenge. Current methods typically rely on extensive multi-view capture setups or monocular videos with per-identity optimization during inference, limiting their scalability and ease of use on unseen subjects. To overcome these efficiency drawbacks, we propose FastGHA, a feed-forward method to generate high-quality Gaussian head avatars from only a few input images while supporting real-time animation. Our approach directly learns a per-pixel Gaussian representation from the input images, and aggregates multi-view information using a transformer-based encoder that fuses image features from both DINOv3 and Stable Diffusion VAE. For real-time animation, we extend the explicit Gaussian representations with per-Gaussian features and introduce a lightweight MLP-based dynamic network to predict 3D Gaussian deformations from expression codes. Furthermore, to enhance geometric smoothness of the 3D head, we employ point maps from a pre-trained large reconstruction model as geometry supervision. Experiments show that our approach significantly outperforms existing methods in both rendering quality and inference efficiency, while supporting real-time dynamic avatar animation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13837 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FastGHA: Generalized Few-Shot 3D Gaussian Head Avatars with Real-Time Animation Ji, Xinya Weiss, Sebastian Kansy, Manuel Naruniec, Jacek Cao, Xun Solenthaler, Barbara Bradley, Derek Computer Vision and Pattern Recognition Despite recent progress in 3D Gaussian-based head avatar modeling, efficiently generating high fidelity avatars remains a challenge. Current methods typically rely on extensive multi-view capture setups or monocular videos with per-identity optimization during inference, limiting their scalability and ease of use on unseen subjects. To overcome these efficiency drawbacks, we propose FastGHA, a feed-forward method to generate high-quality Gaussian head avatars from only a few input images while supporting real-time animation. Our approach directly learns a per-pixel Gaussian representation from the input images, and aggregates multi-view information using a transformer-based encoder that fuses image features from both DINOv3 and Stable Diffusion VAE. For real-time animation, we extend the explicit Gaussian representations with per-Gaussian features and introduce a lightweight MLP-based dynamic network to predict 3D Gaussian deformations from expression codes. Furthermore, to enhance geometric smoothness of the 3D head, we employ point maps from a pre-trained large reconstruction model as geometry supervision. Experiments show that our approach significantly outperforms existing methods in both rendering quality and inference efficiency, while supporting real-time dynamic avatar animation. |
| title | FastGHA: Generalized Few-Shot 3D Gaussian Head Avatars with Real-Time Animation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.13837 |