LPM首次实现工业级生成式视频修复，大幅降低带宽成本。
LPM: Industrial-Scale Generative Video Restoration
- 基于扩散模型统一处理用户视频的复杂退化问题
- 修复后视频占快手总播放时长45%，码率降低20%
- 适合大规模视频平台部署，兼具画质与成本优势
我们提出大型处理模型（LPM），一种基于扩散模型的生成式框架，用于在复杂真实场景退化下实现照片级视频修复。据我们所知，LPM是首个在工业规模部署的生成式视频修复模型。LPM通过大规模数据工程、基础模型训练和高效推理的统一系统，应对用户生成内容（UGC）中的多样化退化。其增强架构、渐进式训练策略与时空金字塔推理机制共同实现任意长度视频的高保真、时序一致修复，覆盖UGC平台广泛内容分布。LPM已在快手投入生产，经模型处理的视频约占总播放时长的45%，在关键体验指标上持续提升。除视觉质量优化外，LPM带来显著系统级收益：在相当感知质量下，相比快手自研编码器，码率降低20%，年均节省带宽成本达数亿级别。其低服务成本也使其成功集成至Kling等产品，证明生成式修复可在大规模视频处理中实现实用、可扩展与低成本。
原文摘要 · Abstract (English)
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. LPM addresses the diverse degradations in user-generated content (UGC) through a unified system encompassing large-scale data engineering, foundation-model training, and efficient inference. Its enhanced architecture, progressive training strategy, and temporal-pyramid inference mechanism jointly enable high-fidelity, temporally consistent restoration of arbitrarily long videos across the broad content distribution encountered on UGC platforms. LPM has been deployed in production at Kuaishou, where videos processed by the model account for approximately 45% of total viewing time, delivering consistent improvements across key quality-of-experience metrics. Beyond perceptual enhancement, LPM delivers substantial system-level benefits: at comparable perceptual quality, it reduces bitrate by 20% relative to Kuaishou's in-house codec, yielding annual bandwidth cost savings on the order of hundreds of millions. Its low serving cost also enables integration into products such as Kling, demonstrating that generative restoration can be practical, scalable, and cost-effective for large-scale video processing.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。