FastVR is a streaming video restoration framework that uses a one‑step diffusion model to achieve strong restoration quality and temporal consistency while processing 1080p video at 11 FPS on a single H20 GPU. It addresses efficiency bottlenecks by combining a lightweight VAE with chunk‑wise causal attention, and improves inference speed and restoration quality through velocity consistency regularization and continuous trajectory learning during training. Experiments demonstrate that FastVR outperforms diffusion baselines in efficiency and achieves state‑of‑the‑art performance on both synthetic and real‑world benchmarks.
By Xiaoxu Chen, Qin Yang, Haoran Bai, Sibin Deng, Ying Chen
arXiv:2605.20624v2 Announce Type: replace-cross
Abstract: Diffusion models provide powerful priors for zero-shot video inverse problems, but their real-time deployment is hindered by two inefficienci...
By Taesung Kwon, Jonghyun Park, Hyungjin Chung, Jong Chul Ye
We present the Large Processing Model (LPM), a diffusion-based generative framework for photorealistic video restoration under complex, in-the-wild degradations. To our knowledge, LPM is the first generative video restoration model deployed at industrial scale.
arXiv:2608.20515v1 Announce Type: new
Abstract: Generative video compression can recover rich visual details at low bitrates, but simultaneously achieving high temporal consistency and low inference...
By Wenzhuo Ma, Zhenzhong Chen
arXiv:2512.23709v3 Announce Type: replace
Abstract: Diffusion-based video super-resolution (VSR) methods deliver strong perceptual quality but are often unsuitable for latency-sensitive scenarios due...
By Hau-Shiang Shiu, Chin-Yang Lin, Zhixiang Wang, Chi-Wei Hsiao, Po-Fan Yu, Yu-Chih Chen, Yu-Lun Liu
The paper introduces VoRTeC, a video compression framework that leverages a foundational flow model to encode latent video representations compactly and predict their positions along flow trajectories. By integrating multi‑scale priors and avoiding access to flow‑matching network parameters, VoRTeC achieves one‑step decoding with high perceptual fidelity, while maintaining temporal consistency through tail‑frame reuse and prior caching. Experiments show a 58% reduction in bit consumption compared to prior diffusion‑based methods and a decoding speed increase ranging from 3 to 197 times, reaching 13 FPS at 720p and 32 FPS at 480p.
By Yichong Xia, Qinhong Wu, Qinhong Wu, Jinpeng Wang, Zeyuan Chen, Haoqian Wang
Real-time video editing requires low-latency causal generation with bounded computational resources while preserving source fidelity and long-term temporal consistency. We present JoyAI-Video-Edit, a 16B-parameter autoregressive diffusion framework for real-time, open-ended video editing without access to future frames or a predefined video duration.
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
arXiv:2607. 20628v1 Announce Type: cross Abstract: Real-world video deblurring remains challenging due to diverse motion patterns, complex degradations, and the scarcity of realistic training data, yet robust restoration is critical for downstream pipelines such as mobile imaging and 3D reconstruction.
By Renbiao Jin, Mingxin Yang, Yutian Chen, Junhao Zhuang, Xin Cai, Mulin Yu, Linning Xu, Wenxian Yu, Danping Zou, Shi Guo, Tianfan Xue
arXiv:2403. 07711v5 Announce Type: replace-cross Abstract: Given the remarkable achievements in image generation through diffusion models, the research community has shown increasing interest in extending these models to video generation.
By Yuta Oshima, Shohei Taniguchi, Masahiro Suzuki, Yutaka Matsuo
Real-time video restoration (VR) for live streams requires high-resolution outputs under strict per-frame latency constraints. Existing one-step diffusion-based VR models remain difficult to deploy on consumer-grade GPUs due to two main bottlenecks: quadratic spatial attention at high resolutions and the latency-memory overhead of large video autoencoders.
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.
By Yushe Cao, Shikun Feng, Ruxiang Duan, Liyong Wang, Dianxi Shi, Chun Yu, Junliang Xing