ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation

• URL: http://arxiv.org/abs/2310.07697v2
• Date: Thu, 23 May 2024 13:58:11 GMT
• Title: ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation
• Authors: Bo Peng, Xinyuan Chen, Yaohui Wang, Chaochao Lu, Yu Qiao,
• Abstract summary: We introduce ConditionVideo, a training-free approach to text-to-video generation based on the provided condition, video, and input text. ConditionVideo generates realistic dynamic videos from random noise or given scene videos. Our method exhibits superior performance in terms of frame consistency, clip score, and conditional accuracy, outperforming other compared methods.
• Score: 33.37279673304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent works have successfully extended large-scale text-to-image models to the video domain, producing promising results but at a high computational cost and requiring a large amount of video data. In this work, we introduce ConditionVideo, a training-free approach to text-to-video generation based on the provided condition, video, and input text, by leveraging the power of off-the-shelf text-to-image generation methods (e.g., Stable Diffusion). ConditionVideo generates realistic dynamic videos from random noise or given scene videos. Our method explicitly disentangles the motion representation into condition-guided and scenery motion components. To this end, the ConditionVideo model is designed with a UNet branch and a control branch. To improve temporal coherence, we introduce sparse bi-directional spatial-temporal attention (sBiST-Attn). The 3D control network extends the conventional 2D controlnet model, aiming to strengthen conditional generation accuracy by additionally leveraging the bi-directional frames in the temporal domain. Our method exhibits superior performance in terms of frame consistency, clip score, and conditional accuracy, outperforming other compared methods.

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