Uni4D: Unifying Visual Foundation Models for 4D Modeling from a Single Video
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912297641836544 |
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| author | Yao, David Yifan Zhai, Albert J. Wang, Shenlong |
| author_facet | Yao, David Yifan Zhai, Albert J. Wang, Shenlong |
| contents | This paper presents a unified approach to understanding dynamic scenes from casual videos. Large pretrained vision foundation models, such as vision-language, video depth prediction, motion tracking, and segmentation models, offer promising capabilities. However, training a single model for comprehensive 4D understanding remains challenging. We introduce Uni4D, a multi-stage optimization framework that harnesses multiple pretrained models to advance dynamic 3D modeling, including static/dynamic reconstruction, camera pose estimation, and dense 3D motion tracking. Our results show state-of-the-art performance in dynamic 4D modeling with superior visual quality. Notably, Uni4D requires no retraining or fine-tuning, highlighting the effectiveness of repurposing visual foundation models for 4D understanding. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_21761 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uni4D: Unifying Visual Foundation Models for 4D Modeling from a Single Video Yao, David Yifan Zhai, Albert J. Wang, Shenlong Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning This paper presents a unified approach to understanding dynamic scenes from casual videos. Large pretrained vision foundation models, such as vision-language, video depth prediction, motion tracking, and segmentation models, offer promising capabilities. However, training a single model for comprehensive 4D understanding remains challenging. We introduce Uni4D, a multi-stage optimization framework that harnesses multiple pretrained models to advance dynamic 3D modeling, including static/dynamic reconstruction, camera pose estimation, and dense 3D motion tracking. Our results show state-of-the-art performance in dynamic 4D modeling with superior visual quality. Notably, Uni4D requires no retraining or fine-tuning, highlighting the effectiveness of repurposing visual foundation models for 4D understanding. |
| title | Uni4D: Unifying Visual Foundation Models for 4D Modeling from a Single Video |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.21761 |