MotionCrafter: Dense Geometry and Motion Reconstruction with a 4D VAE
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914428698492928 |
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| author | Zhu, Ruijie Lu, Jiahao Hu, Wenbo Han, Xiaoguang Cai, Jianfei Shan, Ying Zheng, Chuanxia |
| author_facet | Zhu, Ruijie Lu, Jiahao Hu, Wenbo Han, Xiaoguang Cai, Jianfei Shan, Ying Zheng, Chuanxia |
| contents | We present MotionCrafter, a framework that leverages video generators to jointly reconstruct 4D geometry and estimate dense motion from a monocular video. The key idea is a joint representation of dense 3D point maps and 3D scene flows in a shared coordinate system, together with a 4D VAE tailored to learn this representation effectively. Unlike prior work that strictly aligns 3D values and latents with RGB VAE latents-despite their fundamentally different distributions-we show that such alignment is unnecessary and can hurt performance. Instead, we propose a new data normalization and VAE training strategy that better transfers diffusion priors and greatly improves reconstruction quality. Extensive experiments on multiple datasets show that MotionCrafter achieves state-of-the-art performance in both geometry reconstruction and dense scene flow estimation, delivering 38.64% and 25.0% improvements in geometry and motion reconstruction, respectively, all without any post-optimization. Project page: https://ruijiezhu94.github.io/MotionCrafter_Page |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_08961 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MotionCrafter: Dense Geometry and Motion Reconstruction with a 4D VAE Zhu, Ruijie Lu, Jiahao Hu, Wenbo Han, Xiaoguang Cai, Jianfei Shan, Ying Zheng, Chuanxia Computer Vision and Pattern Recognition Artificial Intelligence Computational Geometry Machine Learning We present MotionCrafter, a framework that leverages video generators to jointly reconstruct 4D geometry and estimate dense motion from a monocular video. The key idea is a joint representation of dense 3D point maps and 3D scene flows in a shared coordinate system, together with a 4D VAE tailored to learn this representation effectively. Unlike prior work that strictly aligns 3D values and latents with RGB VAE latents-despite their fundamentally different distributions-we show that such alignment is unnecessary and can hurt performance. Instead, we propose a new data normalization and VAE training strategy that better transfers diffusion priors and greatly improves reconstruction quality. Extensive experiments on multiple datasets show that MotionCrafter achieves state-of-the-art performance in both geometry reconstruction and dense scene flow estimation, delivering 38.64% and 25.0% improvements in geometry and motion reconstruction, respectively, all without any post-optimization. Project page: https://ruijiezhu94.github.io/MotionCrafter_Page |
| title | MotionCrafter: Dense Geometry and Motion Reconstruction with a 4D VAE |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computational Geometry Machine Learning |
| url | https://arxiv.org/abs/2602.08961 |