arXiv Computer Vision
Looking Ahead: Modeling Future Evolution in Autoregressive Video Generation
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Connected Self Forcing: Beyond Local Learning in Video Autoregression
The paper introduces Connected Self Forcing, a training framework that reconnects gradient paths across autoregressive video chunks to improve long‑horizon visual quality and temporal consistency. It allows feedback from later predictions to influence earlier context generation, using shortcut gradient replay to keep memory usage manageable. The method is combined with distribution‑matching distillation and shows experimental gains without altering the inference procedure.