Consistent Video-to-Video Translation via Explicit Correspondences

Interactive video-to-video applications require real-time gen-eration while maintaining long-range temporal consistency. However, re-cent methods achieve speed by restricting attention to a fixed-lengthcausal window. This limits long-range consistency whenever relevant his-tory falls outside the context window. To address this limitation, we in-troduce vid2vid-long, a correspondence-based approach grounded in asimple observation. Video-to-video translation provides an explicit cross-frame reference: the regions that match in the input video should matchin the output video too. Concretely, we compute patch-level correspon-dences in the input, guiding retrieval of previously generated outputs inthe corresponding regions, and adding them to the context window. Ourexperiments demonstrate that vid2vid-long consistently improves long-range temporal consistency across recent video models while preservinggeneration quality and real-time performance.