FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis

TL;DR

FlowVid introduces a flexible, efficient, and high-quality video-to-video synthesis method that maintains temporal consistency by leveraging imperfect optical flow and editing the first frame with existing image-to-image models.

Contribution

The paper presents FlowVid, a novel V2V synthesis framework that effectively handles optical flow imperfections and enables seamless editing of the first frame for consistent video generation.

Findings

FlowVid is 3.1x to 10.5x faster than prior methods.

It outperforms existing models in user preference studies.

FlowVid supports various modifications like stylization and object swaps.

Abstract

Diffusion models have transformed the image-to-image (I2I) synthesis and are now permeating into videos. However, the advancement of video-to-video (V2V) synthesis has been hampered by the challenge of maintaining temporal consistency across video frames. This paper proposes a consistent V2V synthesis framework by jointly leveraging spatial conditions and temporal optical flow clues within the source video. Contrary to prior methods that strictly adhere to optical flow, our approach harnesses its benefits while handling the imperfection in flow estimation. We encode the optical flow via warping from the first frame and serve it as a supplementary reference in the diffusion model. This enables our model for video synthesis by editing the first frame with any prevalent I2I models and then propagating edits to successive frames. Our V2V model, FlowVid, demonstrates remarkable properties: (1) Flexibility: FlowVid works seamlessly with existing I2I models, facilitating various modifications, including stylization, object swaps, and local edits. (2) Efficiency: Generation of a 4-second video with 30 FPS and 512x512 resolution takes only 1.5 minutes, which is 3.1x, 7.2x, and 10.5x faster than CoDeF, Rerender, and TokenFlow, respectively. (3) High-quality: In user studies, our FlowVid is preferred 45.7% of the time, outperforming CoDeF (3.5%), Rerender (10.2%), and TokenFlow (40.4%).