arXiv AI
Aug 6
Seeking Physics in Diffusion Noise
arXiv:2603. 14294v3 Announce Type: replace-cross Abstract: Do video diffusion models encode signals predictive of physical plausibility?
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.
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arXiv Machine Learning
Jul 30
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.
Hugging Face Trending Papers
Jul 9
OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators
We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR video generators can produce long videos with low latency, but still suffer from error accumulation and weakened motion dynamics during long autoregressive rollout.
Hugging Face Trending Papers
Jun 17
UniTemp: Unlocking Video Generation in Any Temporal Order via Bidirectional Distillation
Autoregressive video diffusion models have emerged as a promising approach for long video generation, achieving strong performance in streaming settings. However, existing methods are restricted to forward temporal generation, whereas practical video creation often requires flexible generation order, e.
arXiv Machine Learning
Jun 25
Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models
arXiv:2606. 25473v1 Announce Type: cross Abstract: Autoregressive video diffusion with causal diffusion transformers has emerged as a major paradigm for real-time streaming video generation and action-conditioned interactive world models.
arXiv AI
Aug 7
In-Context Forcing: Uncovering Context Effects in Autoregressive Video Diffusion
arXiv:2608. 05237v1 Announce Type: cross Abstract: Current few-step autoregressive video diffusion models depend on previous fully denoised clean frames as context for all denoising steps of the current frame.
arXiv AI
1d ago
Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning
arXiv:2608. 14706v1 Announce Type: cross Abstract: Standard autoregressive video generation algorithms based on Diffusion and Flow Matching rely on rigid training objectives and static sampling schedules, limiting inference procedures from adapting to the data.
arXiv AI
Jun 12
Diffusion Transformer World-Action Model for AV Scene Prediction
arXiv:2606. 12987v1 Announce Type: cross Abstract: Action-conditioned world models let an autonomous vehicle predict future camera scenes from its own planned controls, enabling planning and simulation without real-world rollouts, but at compact, trainable scale the futures are ambiguous and the field's standard distortion metrics actively mislead: they reward a blurry regression mean over a realistic prediction.
arXiv AI
Jul 3
NeoMap: Training-free Novel-View Synthesis from Single Images and Videos
arXiv:2607. 01962v1 Announce Type: cross Abstract: We study the challenging problem of novel view video synthesis from single images or monocular videos.
arXiv Machine Learning
Jul 23
Diffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction
arXiv:2607. 19919v1 Announce Type: cross Abstract: We propose Diffusion ReRoll, a diffusion-based framework for robotic sequential prediction that enables revisable denoising over horizons.
Hugging Face Trending Papers
Jul 2
NeoMap: Training-free Novel-View Synthesis from Single Images and Videos
We study the challenging problem of novel view video synthesis from single images or monocular videos. Existing methods, which operate under the assumption that pre-trained video models lack native novel view synthesis capability and enforce view alignment via camera conditioning, task-specific fine-tuning, or stepwise hard denoising guidance, often suffer from artifacts and compromised global scene consistency.
arXiv Machine Learning
Jun 5
Flash-WAM: Modality-Aware Distillation for World Action Models
arXiv:2606. 05254v1 Announce Type: new Abstract: World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achieving strong performance on manipulation benchmarks but requiring tens of denoising steps, a cost that precludes real-time control.