Unified Video Editing with Temporal Reasoner
Existing video editing methods face a critical trade-off: expert models offer precision but rely on task-specific priors like masks, hindering unification; conversely, unified temporal in-context learning models are mask-free but lack explicit spatial cues, leading to weak instruction-to-region mapping and imprecise localization. To resolve this conflict, we propose VideoCoF, a novel Chain-of-Frames approach inspired by Chain-of-Thought reasoning. VideoCoF enforces a ``see, reason, then edit" procedure by compelling the video diffusion model to first predict reasoning tokens (edit-region latents) before generating the target video tokens. This explicit reasoning step removes the need for user-provided masks while achieving precise instruction-to-region alignment and fine-grained video editing. Furthermore, we introduce a RoPE alignment strategy that leverages these reasoning tokens to ensure motion alignment and enable length extrapolation beyond the training duration. We demonstrate that with a minimal data cost of only 50k video pairs, VideoCoF achieves state-of-the-art performance on VideoCoF-Bench, validating the efficiency and effectiveness of our approach. Our code, weight, data are available at https://github.com/knightyxp/VideoCoF.
69379e0319d912300c34a2fb
Xiangpeng Yang
claimed_verified
2025-12-09T10:34:33.919Z
6486df66373f79a52913e017
https://cdn-avatars.huggingface.co/v1/production/uploads/6486df66373f79a52913e017/vUncXohxJN4ixXR6QUMxh.jpeg
Xiangpeng Yang
XiangpengYang
user
XiangpengYang
69379e0319d912300c34a2fc
Ji Xie
claimed_verified
2025-12-10T19:41:41.113Z
64e99fc07e2ec711a7138262
https://cdn-avatars.huggingface.co/v1/production/uploads/64e99fc07e2ec711a7138262/9-Y-ZFaUhDpi_8X4oRO5E.jpeg
Ji Xie
sanaka87
user
sanaka87
69379e0319d912300c34a2fd
Yiyuan Yang
69379e0319d912300c34a2fe
Yan Huang
69379e0319d912300c34a2ff
Min Xu
69379e0319d912300c34a300
Qiang Wu
Unified Video Editing with Temporal Reasoner
Existing video editing methods face a critical trade-off: expert models offer precision but rely on task-specific priors like masks, hindering unification; conversely, unified temporal in-context learning models are mask-free but lack explicit spatial cues, leading to weak instruction-to-region mapping and imprecise localization. To resolve this conflict, we propose VideoCoF, a novel Chain-of-Frames approach inspired by Chain-of-Thought reasoning. VideoCoF enforces a ``see, reason, then edit" procedure by compelling the video diffusion model to first predict reasoning tokens (edit-region latents) before generating the target video tokens. This explicit reasoning step removes the need for user-provided masks while achieving precise instruction-to-region alignment and fine-grained video editing. Furthermore, we introduce a RoPE alignment strategy that leverages these reasoning tokens to ensure motion alignment and enable length extrapolation beyond the training duration. We demonstrate that with a minimal data cost of only 50k video pairs, VideoCoF achieves state-of-the-art performance on VideoCoF-Bench, validating the efficiency and effectiveness of our approach. Our code, weight, data are available at https://github.com/knightyxp/VideoCoF.
VideoCoF, a Chain-of-Frames approach, improves video editing precision and instruction-to-region mapping by using reasoning tokens without requiring user-provided masks.
chain-of-frames chain-of-thought reasoning video diffusion model reasoning tokens edit-region latents target video tokens instruction-to-region alignment fine-grained video editing RoPE alignment motion alignment length extrapolation