← 研踪 PaperTrailCVF · CVPR 2026:cvf:0da607e64bb5d667d2fe8ab3c1524c56 · 记录 #1
RFDM: Residual Flow Diffusion Models for Video Editing
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 04:34
摘要
Instructional video editing applies edits to an input video using only text prompts, enabling intuitive natural-language control. Despite the rapid progress, most methods still require fixed-length inputs and substantial compute. Meanwhile, autoregressive video generation enables efficient variable-length synthesis, yet remains under-explored for video editing. We introduce a causal, efficient video editing model that edits variable-length videos frame by frame. For efficiency, we start from a 2D image-to-image (I2I) diffusion model and adapt it to video-to-video (V2V) editing by conditioning the edit at time step t on the model's prediction at t-1. To leverage videos' temporal redundancy, we propose a new I2I diffusion forward process formulation that encourages the model to predict the residual between the target output and the previous prediction. We call this \underline R esidual \underline F low \underline D iffusion \underline M odel (\methodname), which focuses the denoising process on changes between consecutive frames. Moreover, we propose a new benchmark that better ranks state-of-the-art methods by faithfulness for video editing tasks. Trained on paired video data for global/local style transfer and object removal, \methodname surpasses I2I-based methods and competes with fully spatiotemporal (3D) V2V models, while matching the compute of image models and scaling independently of input video length. More content can be found in \href https://smsd75.github.io/RFDM_page/ RFDM page .
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记录 #1 · 2026 · 最新观测版本
RFDM: Residual Flow Diffusion Models for Video Editing
Instructional video editing applies edits to an input video using only text prompts, enabling intuitive natural-language control. Despite the rapid progress, most methods still require fixed-length inputs and substantial compute. Meanwhile, autoregressive video generation enables efficient variable-length synthesis, yet remains under-explored for video editing. We introduce a causal, efficient video editing model that edits variable-length videos frame by frame. For efficiency, we start from a 2D image-to-image (I2I) diffusion model and adapt it to video-to-video (V2V) editing by conditioning the edit at time step t on the model's prediction at t-1. To leverage videos' temporal redundancy, we propose a new I2I diffusion forward process formulation that encourages the model to predict the residual between the target output and the previous prediction. We call this \underline R esidual \underline F low \underline D iffusion \underline M odel (\methodname), which focuses the denoising process on changes between consecutive frames. Moreover, we propose a new benchmark that better ranks state-of-the-art methods by faithfulness for video editing tasks. Trained on paired video data for global/local style transfer and object removal, \methodname surpasses I2I-based methods and competes with fully spatiotemporal (3D) V2V models, while matching the compute of image models and scaling independently of input video length. More content can be found in \href https://smsd75.github.io/RFDM_page/ RFDM page .
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