arXiv Computer Vision
SAGA: Stable Acceleration Guidance for Autoregressive Video Generation
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Mitigating Compounding Error via Video Representation Regularization
arXiv:2607. 27036v1 Announce Type: cross Abstract: Video diffusion-based world models enable long autoregressive video generation for robotics, autonomous driving and simulation tasks, yet sliding-window autoregressive inference suffers from severe error accumulation that degrades frame quality over time.
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Relax Forcing: Relaxed KV-Memory for Consistent Long Video Generation
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Steady-Forcing: Balancing Spatial Persistence and Motion Continuity in Long-Horizon Nature Video Diffusion
arXiv:2606. 14732v1 Announce Type: cross Abstract: Autoregressive video diffusion models enable streaming generation but often degrade over long rollouts: static scene layouts drift, while mechanisms that improve spatial stability tend to suppress motion, causing natural flows such as water, fire, or smoke to stagnate.
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Manifold4D: Denoising on Point Cloud Rendered Manifolds for Video Re-shooting
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Test-Time Noise Guided Adaptation for Realistic Autoregressive Video Generation
arXiv:2607. 15849v1 Announce Type: cross Abstract: Autoregressive video diffusion models have enabled the generation of arbitrarily long videos by removing conditioning on future frames, thus greatly improving computational efficiency.
arXiv AI
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Seeking Physics in Diffusion Noise
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