EditStream: A Unified Autoregressive Framework for Interactive Video Generation and Editing

Interactive video generation and editing are becoming increasingly important for creative design. In this paper, we introduce EditStream—a unified framework for interactive video generation and editing. EditStream unifies Text-to-Video, Video-to-Video, Editing Propagation, Reference-guided Video Editing, and Camera Pose Change, enabling flexible control over video generation, transformation, and editing within one system. To make the unified model practical for interactive use, we develop a two-stage distillation approach that combines Velocity Moment Matching (VMM) with autoregressive unrolling. VMM matches conditional velocity moments at student-reached intermediate states to preserve generation quality and motion, while unrolling exposes the student to its own autoregressive predictions to improve temporal stability. They alleviate common challenges in few-step autoregressive video generation, including over-saturation, degraded motion, temporal instability, and complex training. EditStream provides a practical and scalable solution that bridges high-quality multi-task diffusion-based video models with interactive creative workflows.