Motion-Catcher: Upholding Motion and Content Consistency in Multi-Sequence Video Generation
Keywords: Diffusion models, video generation
Abstract: Recent developments in diffusion models have significantly advanced the field of video generation. However, technical challenges still exist in terms of spatiotemporal continuity and content consistency in long video generation. In this paper, we propose Motion-Catcher, a diffusion model-based method for multi-sequence video generation that aims to address the issues of motion inconsistency and content degradation. By incorporating a motion capture module, the model leverages optical flow information from video sequences to capture both local and global movements, enhancing the motion consistency of the videos. Furthermore, a dynamic content prior module is proposed to monitor regions prone to degradation, which helps maintain content consistency throughout the generated videos. Extensive experiments have validated that the proposed Motion-Catcher can generate videos with higher quality in terms of motion continuity and consistency. The source code and additional experimental results are available at https://github.com/YuukiGong/Motion-Catcher.
Supplementary Material: zip
Primary Area: generative models
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Submission Number: 5993
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