AI RESEARCH
DCARL: A Divide-and-Conquer Framework for Autoregressive Long-Trajectory Video Generation
arXiv CS.CV
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ArXi:2603.24835v1 Announce Type: new Long-trajectory video generation is a crucial yet challenging task for world modeling primarily due to the limited scalability of existing video diffusion models (VDMs). Autoregressive models, while offering infinite rollout, suffer from visual drift and poor controllability. To address these issues, we propose DCARL, a novel divide-and-conquer, autoregressive framework that effectively combines the structural stability of the divide-and-conquer scheme with the high-fidelity generation of VDMs.