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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.06208 |
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| _version_ | 1866918155974082560 |
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| author | Yenphraphai, Jiraphon Mirzaei, Ashkan Chen, Jianqi Zou, Jiaxu Tulyakov, Sergey Yeh, Raymond A. Wonka, Peter Wang, Chaoyang |
| author_facet | Yenphraphai, Jiraphon Mirzaei, Ashkan Chen, Jianqi Zou, Jiaxu Tulyakov, Sergey Yeh, Raymond A. Wonka, Peter Wang, Chaoyang |
| contents | Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. In this work, we introduce a native video-to-4D shape generation framework that synthesizes a single dynamic 3D representation end-to-end from the video. Our framework introduces three key components based on large-scale pre-trained 3D models: (i) a temporal attention that conditions generation on all frames while producing a time-indexed dynamic representation; (ii) a time-aware point sampling and 4D latent anchoring that promote temporally consistent geometry and texture; and (iii) noise sharing across frames to enhance temporal stability. Our method accurately captures non-rigid motion, volume changes, and even topological transitions without per-frame optimization. Across diverse in-the-wild videos, our method improves robustness and perceptual fidelity and reduces failure modes compared with the baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06208 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ShapeGen4D: Towards High Quality 4D Shape Generation from Videos Yenphraphai, Jiraphon Mirzaei, Ashkan Chen, Jianqi Zou, Jiaxu Tulyakov, Sergey Yeh, Raymond A. Wonka, Peter Wang, Chaoyang Computer Vision and Pattern Recognition Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. In this work, we introduce a native video-to-4D shape generation framework that synthesizes a single dynamic 3D representation end-to-end from the video. Our framework introduces three key components based on large-scale pre-trained 3D models: (i) a temporal attention that conditions generation on all frames while producing a time-indexed dynamic representation; (ii) a time-aware point sampling and 4D latent anchoring that promote temporally consistent geometry and texture; and (iii) noise sharing across frames to enhance temporal stability. Our method accurately captures non-rigid motion, volume changes, and even topological transitions without per-frame optimization. Across diverse in-the-wild videos, our method improves robustness and perceptual fidelity and reduces failure modes compared with the baselines. |
| title | ShapeGen4D: Towards High Quality 4D Shape Generation from Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.06208 |