Video generation and editing conditioned on text prompts or images have undergone significant advancements. However, challenges remain in accurately controlling global layout and geometry details solely by texts, and supporting motion control and local modification through images. In this paper, we aim to achieve sketch-based spatial and motion control for video generation and support fine-grained editing of real or synthetic videos. Based on the DiT video generation model, we propose a memory-efficient control structure with sketch control blocks that predict residual features of skipped DiT blocks. Sketches are drawn on one or two keyframes (at arbitrary time points) for easy interaction. To propagate such temporally sparse sketch conditions across all frames, we propose an inter-frame attention mechanism to analyze the relationship between the keyframes and each video frame. For sketch-based video editing, we design an additional video insertion module that maintains consistency between the newly edited content and the original video's spatial feature and dynamic motion. During inference, we use latent fusion for the accurate preservation of unedited regions. Extensive experiments demonstrate that our SketchVideo achieves superior performance in controllable video generation and editing.
@inproceedings{Liu2025sketchvideo,
author = {Liu, Feng-Lin and Fu, Hongbo and Wang, Xintao and Ye, Weicai and Wan, Pengfei and Zhang, Di and Gao, Lin},
title = {SketchVideo: Sketch-based Video Generation and Editing},
booktitle = {{IEEE/CVF} Conference on Computer Vision and Pattern Recognition},
publisher = {{IEEE}},
year = {2025},
}