ViRDM is a new post‑training method for few‑step causal video generation that eliminates the need for a large teacher model and an online critic. By applying representation distribution matching (RDM) with a precomputed target distribution, a lightweight VAE decoder, and staged vector–Jacobian products, ViRDM overcomes memory, optimization, and temporal dynamics challenges. The approach reduces GPU memory usage and training time, achieving state‑of‑the‑art VBench performance with only 20 generator updates and 16 A100 GPU‑hours.
By Zichong Meng, Chongjian Ge, Chun-Hao P. Huang, Yang Zhou, Huaizu Jiang
arXiv:2609.37925v1 Announce Type: cross
Abstract: Autoregressive (AR) video diffusion enables low-latency, streamable video generation, but prediction errors often accumulate over long rollouts. Trai...
By Chenjian Gao, Zhihao Hu, Jianqi Ma, Jun Zhang, Weidong Zhang, Tianfan Xue
Few-step autoregressive (AR) video diffusion enables low-latency streaming generation, but existing post-training methods predominantly rely on Distribution Matching Distillation (DMD), requiring both...
arXiv:2610.00812v1 Announce Type: cross
Abstract: Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamic...
By Chaoyu Li, Xiaoyi Gu, Yogesh Kulkarni, Eun Woo Im, Mohammadmahdi Honarmand, Zeyu Wang, Juntong Song, Fei Du, Xilin Jiang, Kexin Zheng, Tianzhi Li, Fei Tao, Pooyan Fazli
The paper introduces FreeMatching, a framework that moves beyond traditional spatio-temporal priors for dense correspondence matching. It combines generative and semantic representations with diverse supervision sources, and uses teacher‑guided refinement to improve identity‑preserving correspondence in image editing and reference‑guided generation tasks. Experiments show that a single FreeMatching model outperforms existing methods on challenging image pairs while maintaining strong performance on classical benchmarks, and its scores correlate with human judgment of identity preservation.
By Luping Liu, Bingyi Kang, Yifan Wang, Dong Xu
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.