AI RESEARCH

CausalCine: Real-Time Autoregressive Generation for Multi-Shot Video Narratives

arXiv CS.CV

ArXi:2605.12496v1 Announce Type: new Autoregressive video generation aims at real-time, open-ended synthesis. Yet, cinematic storytelling is not merely the endless extension of a single scene; it requires progressing through evolving events, viewpoint shifts, and discrete shot boundaries. Existing autoregressive models often struggle in this setting. Trained primarily for short-horizon continuation, they treat long sequences as extended single shots, inevitably suffering from motion stagnation and semantic drift during long rollouts. To bridge this gap, we.