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[pdf]
[arXiv]
[bibtex]@InProceedings{Yu_2025_ICCV, author = {Yu, Mark and Hu, Wenbo and Xing, Jinbo and Shan, Ying}, title = {TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2025}, pages = {100-111} }
TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models
Abstract
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. Code and pre-trained model will be released.
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