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.

翻译：视频能否以低至0.01%的极端压缩率进行压缩？为此，我们通过引入生成式视频压缩（GVC）这一新框架，在某些情况下实现了0.02%的压缩率。该框架通过利用现代生成式视频模型，在保持以感知为中心、面向任务的通信范式（对应于香农-韦弗模型的C层级）的同时，重新定义了视频压缩的极限。此外，我们如何在压缩率或带宽与计算之间进行权衡？GVC通过将负担从传输转移到推理来回答这个问题：它将视频编码为极其紧凑的表示，并将内容重建任务委托给接收端，在那里强大的生成先验能够从传输的极少信息中合成高质量视频。GVC是否实用且可部署？为确保实际部署，我们提出了一种压缩-计算权衡策略，使其能够在消费级GPU上实现快速推理。在AI Flow框架内，GVC为应急救援、远程监控和移动边缘计算等带宽与资源受限环境中的视频通信开辟了新的可能性。通过实证验证，我们证明GVC为迈向一种新的高效、可扩展且实用的视频通信范式提供了可行的路径。