arXiv AI
Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video Diffusion
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FrameMorrow: Future-guided Frame Selection with Prospective Tokens for Long-Horizon Video Generation
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Connected Self Forcing: Beyond Local Learning in Video Autoregression
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Next Forcing: Causal World Modeling with Multi-Chunk Prediction
Autoregressive video generation has emerged as a powerful paradigm for World Action Models (WAMs). However, existing approaches suffer from slow training convergence and limited converged accuracy, particularly at high frame rates, as the training supervision is confined to the current chunk without explicit signals about future dynamics; they also suffer from slow inference due to iterative video denoising.
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Relax Forcing: Relaxed KV-Memory for Consistent Long Video Generation
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