The Seriality Gap in Video Diffusion Models
arXiv:2607. 13031v1 Announce Type: new Abstract: When one ball strikes another, then another, video models should predict the consequences of each bounce.
When one ball strikes another, then another, video models should predict the consequences of each bounce. In controlled experiments on multi-ball hard-sphere dynamics, we find that the performance of standard bidirectional video diffusion degrades as the causal chain lengthens, even when provided more denoising steps.
arXiv:2607. 13031v1 Announce Type: new Abstract: When one ball strikes another, then another, video models should predict the consequences of each bounce.
arXiv:2406.04814v4 Announce Type: replace-cross Abstract: Video diffusion models can enable embodied agents to anticipate plausible futures from the recent past, but they are typically trained offlin...
S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation introduces a hybrid diffusion approach that first applies autoregressive diffusion at high noise levels and then switches to parallel diffusion at low noise levels. This method coordinates interdependent events to produce valid state transitions while reducing sampling time compared to fully serial generation. Implemented with a pixel‑space diffusion transformer and a LoRA‑fine‑tuned pretrained video model, S2PD outperforms bidirectional baselines in rule adherence and achieves greater temporal stability and sampling efficiency across games, physical simulations, and real video.
LiveVVT introduces a rolling streaming diffusion framework for video virtual try‑on that maintains high visual fidelity while enabling real‑time performance. It preserves bounded bidirectional modeling within a fixed‑size window, emits clean video chunks iteratively, and uses two memory modules—a bounded temporal memory and a persistent global appearance memory—to sustain long‑term consistency. A progressive distillation process further aligns teacher‑based bidirectional learning with causal few‑step inference, resulting in superior generation quality with 26× lower latency and 11× higher throughput compared to comparable models.
arXiv:2609.23658v1 Announce Type: cross Abstract: Despite impressive visual quality, state-of-the-art video diffusion models often generate content that violates real-world physical laws. While exist...
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.
LiveVVT introduces a rolling streaming diffusion framework for video virtual try‑on that maintains high visual fidelity while enabling real‑time performance. By confining bidirectional spatio‑temporal modeling to a fixed‑size window and using bounded temporal and global appearance memories, it emits clean video chunks with low latency. A progressive distillation pipeline further refines the model, achieving superior quality with 26× lower latency and 11× higher throughput compared to prior methods.
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.
The paper introduces Relax Forcing, a training‑free memory mechanism for autoregressive video diffusion that structures temporal context into Sink, Tail, and History frames. By selecting History frames with a relaxation criterion, the method reduces error accumulation and attention overhead while preserving motion dynamics. Experiments on VBench‑Long demonstrate that this structured memory improves long‑video generation quality over existing baselines.
arXiv:2603. 14294v3 Announce Type: replace-cross Abstract: Do video diffusion models encode signals predictive of physical plausibility?
arXiv:2610.03154v1 Announce Type: cross Abstract: Video generation models produce strikingly realistic sequences and are increasingly proposed as world models, yet recent benchmarks reveal pronounced...
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.