# Generative Video Compression: Towards 0.01% Compression Rate for Video Transmission

arXiv: https://arxiv.org/abs/2512.24300 · HTML: https://mlsanity.com/paper/2512.24300/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 30 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)
- [AI Flow at the Network Edge](https://mlsanity.com/paper/2411.12469/) (2024)
- [AI Flow: Perspectives, Scenarios, and Approaches](https://mlsanity.com/paper/2506.12479/) (2025)
- [Task-Oriented Feature Compression for Multimodal Understanding via Device-Edge Co-Inference](https://mlsanity.com/paper/2503.12926/) (2025)
- [Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compression](https://mlsanity.com/paper/2511.08066/) (2025)

## Built on by (most cited first, 12 of 12)

- [GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow](https://mlsanity.com/paper/2603.26571/) (2026)
- [PhysOmni: Physics-Grounded Multi-Object Scene Generation from a Single Image with Real-Time Interaction](https://mlsanity.com/paper/2605.20290/) (2026)
- [Enhancing Neural Video Compression of Static Scenes with Positive-Incentive Noise](https://mlsanity.com/paper/2603.06095/) (2026)
- [Reward-Aware Trajectory Shaping for Few-step Visual Generation](https://mlsanity.com/paper/2604.14910/) (2026)
- [Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation](https://mlsanity.com/paper/2604.19234/) (2026)
- [Generative Transmission: Rethinking Computation, Bandwidth, and Memory in Communication](https://mlsanity.com/paper/2607.17482/) (2026)
- [Recent Advances of End-to-End Video Coding Technologies for AVS Standard Development](https://mlsanity.com/paper/2602.00483/) (2026)
- [An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation](https://mlsanity.com/paper/2606.00987/) (2026)
- [Semantic-Aware Temporal Adaptation for UAV Anti-UAV Tracking](https://mlsanity.com/paper/2607.26511/) (2026)
- [CodecArena: Codec Quality Assessment via Visual Reinforcement Learning](https://mlsanity.com/paper/2608.09139/) (2026)
- [Uncertainty DMD: Restoring Diversity in Few-Step Autoregressive Video Distillation](https://mlsanity.com/paper/2609.11265/) (2026)
- [Information Capacity of Generative Video Compression: Quantifying the Rate-Compute Exchange at Identical Quality](https://mlsanity.com/paper/2609.27493/) (2026)

## Abstract (arXiv)

Whether a video can be compressed at an extreme compression rate as low as 0.01%? To this end, we achieve the compression rate as 0.02% at some cases by introducing Generative Video Compression (GVC), a new framework that redefines the limits of video compression by leveraging modern generative video models to achieve extreme compression rates while preserving a perception-centric, task-oriented communication paradigm, corresponding to Level C of the Shannon-Weaver model. Besides, How we trade computation for compression rate or bandwidth? GVC answers this question by shifting the burden from transmission to inference: it encodes video into extremely compact representations and delegates content reconstruction to the receiver, where powerful generative priors synthesize high-quality video from minimal transmitted information. Is GVC practical and deployable? To ensure practical deployment, we propose a compression-computation trade-off strategy, enabling fast inference on consume-grade GPUs. Within the AI Flow framework, GVC opens new possibility for video communication in bandwidth- and resource-constrained environments such as emergency rescue, remote surveillance, and mobile edge computing. Through empirical validation, we demonstrate that GVC offers a viable path toward a new effective, efficient, scalable, and practical video communication paradigm.