4D-Animal: Freely Reconstructing Animatable 3D Animals from Videos
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908449108918272 |
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| author | Zhong, Shanshan Peng, Jiawei Zheng, Zehan Huang, Zhongzhan Ma, Wufei Zhang, Guofeng Liu, Qihao Yuille, Alan Chen, Jieneng |
| author_facet | Zhong, Shanshan Peng, Jiawei Zheng, Zehan Huang, Zhongzhan Ma, Wufei Zhang, Guofeng Liu, Qihao Yuille, Alan Chen, Jieneng |
| contents | Existing methods for reconstructing animatable 3D animals from videos typically rely on sparse semantic keypoints to fit parametric models. However, obtaining such keypoints is labor-intensive, and keypoint detectors trained on limited animal data are often unreliable. To address this, we propose 4D-Animal, a novel framework that reconstructs animatable 3D animals from videos without requiring sparse keypoint annotations. Our approach introduces a dense feature network that maps 2D representations to SMAL parameters, enhancing both the efficiency and stability of the fitting process. Furthermore, we develop a hierarchical alignment strategy that integrates silhouette, part-level, pixel-level, and temporal cues from pre-trained 2D visual models to produce accurate and temporally coherent reconstructions across frames. Extensive experiments demonstrate that 4D-Animal outperforms both model-based and model-free baselines. Moreover, the high-quality 3D assets generated by our method can benefit other 3D tasks, underscoring its potential for large-scale applications. The code is released at https://github.com/zhongshsh/4D-Animal. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10437 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 4D-Animal: Freely Reconstructing Animatable 3D Animals from Videos Zhong, Shanshan Peng, Jiawei Zheng, Zehan Huang, Zhongzhan Ma, Wufei Zhang, Guofeng Liu, Qihao Yuille, Alan Chen, Jieneng Computer Vision and Pattern Recognition Existing methods for reconstructing animatable 3D animals from videos typically rely on sparse semantic keypoints to fit parametric models. However, obtaining such keypoints is labor-intensive, and keypoint detectors trained on limited animal data are often unreliable. To address this, we propose 4D-Animal, a novel framework that reconstructs animatable 3D animals from videos without requiring sparse keypoint annotations. Our approach introduces a dense feature network that maps 2D representations to SMAL parameters, enhancing both the efficiency and stability of the fitting process. Furthermore, we develop a hierarchical alignment strategy that integrates silhouette, part-level, pixel-level, and temporal cues from pre-trained 2D visual models to produce accurate and temporally coherent reconstructions across frames. Extensive experiments demonstrate that 4D-Animal outperforms both model-based and model-free baselines. Moreover, the high-quality 3D assets generated by our method can benefit other 3D tasks, underscoring its potential for large-scale applications. The code is released at https://github.com/zhongshsh/4D-Animal. |
| title | 4D-Animal: Freely Reconstructing Animatable 3D Animals from Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.10437 |