Highly Detailed and Temporal Consistent Video Stylization via Synchronized Multi-Frame Diffusion
This addresses the challenge of producing coherent and detailed stylized videos for applications in video editing and content creation, representing an incremental improvement over existing diffusion-based approaches.
The paper tackles the problem of generating videos with both high visual detail and temporal consistency in text-guided video-to-video stylization, achieving superior qualitative and quantitative results compared to state-of-the-art methods.
Text-guided video-to-video stylization transforms the visual appearance of a source video to a different appearance guided on textual prompts. Existing text-guided image diffusion models can be extended for stylized video synthesis. However, they struggle to generate videos with both highly detailed appearance and temporal consistency. In this paper, we propose a synchronized multi-frame diffusion framework to maintain both the visual details and the temporal consistency. Frames are denoised in a synchronous fashion, and more importantly, information of different frames is shared since the beginning of the denoising process. Such information sharing ensures that a consensus, in terms of the overall structure and color distribution, among frames can be reached in the early stage of the denoising process before it is too late. The optical flow from the original video serves as the connection, and hence the venue for information sharing, among frames. We demonstrate the effectiveness of our method in generating high-quality and diverse results in extensive experiments. Our method shows superior qualitative and quantitative results compared to state-of-the-art video editing methods.