TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912264732278784 |
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| author | YU, Mark Hu, Wenbo Xing, Jinbo Shan, Ying |
| author_facet | YU, Mark Hu, Wenbo Xing, Jinbo Shan, Ying |
| contents | We present TrajectoryCrafter, a novel approach to redirect camera trajectories for monocular videos. By disentangling deterministic view transformations from stochastic content generation, our method achieves precise control over user-specified camera trajectories. We propose a novel dual-stream conditional video diffusion model that concurrently integrates point cloud renders and source videos as conditions, ensuring accurate view transformations and coherent 4D content generation. Instead of leveraging scarce multi-view videos, we curate a hybrid training dataset combining web-scale monocular videos with static multi-view datasets, by our innovative double-reprojection strategy, significantly fostering robust generalization across diverse scenes. Extensive evaluations on multi-view and large-scale monocular videos demonstrate the superior performance of our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models YU, Mark Hu, Wenbo Xing, Jinbo Shan, Ying Computer Vision and Pattern Recognition Artificial Intelligence Graphics We present TrajectoryCrafter, a novel approach to redirect camera trajectories for monocular videos. By disentangling deterministic view transformations from stochastic content generation, our method achieves precise control over user-specified camera trajectories. We propose a novel dual-stream conditional video diffusion model that concurrently integrates point cloud renders and source videos as conditions, ensuring accurate view transformations and coherent 4D content generation. Instead of leveraging scarce multi-view videos, we curate a hybrid training dataset combining web-scale monocular videos with static multi-view datasets, by our innovative double-reprojection strategy, significantly fostering robust generalization across diverse scenes. Extensive evaluations on multi-view and large-scale monocular videos demonstrate the superior performance of our method. |
| title | TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics |
| url | https://arxiv.org/abs/2503.05638 |