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arXiv AI

VoRTeC: Taming Foundation Flow for One-step Real time Video Compression

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.

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arXiv Computer Vision
Oct 12

Unified Generative Video Compression with Autoregressive and Bidirectional Video Diffusion

The paper introduces UniGVC, a unified generative video codec that supports both low‑delay streaming and high‑quality playback from a single bitstream. It offers two decoding modes: UniGVC‑BI, a bidirectional diffusion model with dense attention for quality‑oriented random‑access decoding, and UniGVC‑AR, a windowed autoregressive diffusion model for low‑delay streaming. Both modes share a hierarchical latent codec that produces ultra‑low‑bitrate conditions, enabling the BI model to act as a teacher for distilling the AR model, while a lightweight conditional decoder further reduces streaming latency.

By Tianyu Zhang, Shuai Huo, Dengchao Jin, Hewei Liu, Meng Lei, Tianwen Wu, Jiawen Gu, Chao Zhou, Li Li, Dong Liu
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arXiv AI
Sep 28

GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow

The paper introduces GVCC, a zero‑shot video compression framework that uses a pretrained generative video model as the decoder. GVCC transforms deterministic rectified‑flow samplers into stochastic processes, enabling the transmission of compressed information through per‑step stochastic innovations. The authors evaluate three GVCC variants—Text‑to‑Video, Image‑to‑Video, and First‑Last‑Frame‑to‑Video—on the UVG dataset, reporting perceptual, fidelity, and temporal metrics without claiming global rate‑distortion gains.

By Ziyue Zeng, Xun Su, Haoyuan Liu, Bingyu Lu, Yui Tatsumi, Hiroshi Watanabe
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arXiv Computer Vision
Oct 5

Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion

ActDiff-VC is a diffusion-based video compression framework designed for ultra‑low‑bitrate scenarios. It partitions videos into variable‑length segments, transmits keyframes only when necessary, and encodes temporal dynamics with a compact set of tracked point trajectories. Conditioned on these sparse signals, a conditional diffusion decoder reconstructs the remaining frames, achieving significant bitrate reductions and perceptual quality gains compared to strong learned codecs.

By Amirhosein Javadi, Shirin Saeedi Bidokhti, Tara Javidi
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arXiv AI
Jul 24

RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring

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
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arXiv Computer Vision
Sep 16

tcnerv:dual-domain temporal context modeling for implicit neural video compression

TCNeRV is a new implicit neural video compression method that models temporal context in both feature and embedding domains. Its multi‑scale temporal‑context fusion module injects gated historical features across decoder scales, while temporal embedding‑residual coding predicts and encodes only the residual of each content embedding. With about 3 million parameters, TCNeRV achieves an average PSNR of 36.08 dB on the UVG dataset, outperforming HNeRV‑Boost by 2.20 dB and reducing BD‑rate by 22.06%, 66.73%, and 29.85% relative to HM, DCVC, and HiNeRV respectively.

By Xuezhi Xiang, Yixin Zhao, Heqi Xiang, Jiayao Liu, Shanjun Zhang
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Hugging Face Trending Papers
Aug 13

V-RAE: Rethinking Video Latent Spaces for Generation

Latent video generation relies on autoencoders to define a compact space in which generative models operate. Although video autoencoder architectures have evolved substantially, their latent spaces are still optimized primarily for pixel-level reconstruction and provide limited high-level semantic organization.

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