ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation
This work addresses the challenge of efficient video generation for AI and creative applications, but it is incremental as it builds on existing text-to-image models.
The paper tackles the problem of high computational cost and data requirements in text-to-video generation by introducing ConditionVideo, a training-free method that uses off-the-shelf text-to-image models to generate realistic dynamic videos from conditions, achieving superior performance in frame consistency, clip score, and conditional accuracy.
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.