LPM: Industrial-Scale Generative Video Restoration

2026-07-15 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

The Large Processing Model (LPM) is a diffusion-based generative framework for photorealistic video restoration, specifically designed for complex, in-the-wild degradations found in user-generated content (UGC). It represents the first generative video restoration model deployed at an industrial scale, notably at Kuaishou. LPM integrates large-scale data engineering, foundation-model training, and efficient inference through an enhanced architecture, progressive training strategy, and temporal-pyramid inference mechanism. This enables high-fidelity, temporally consistent restoration of arbitrarily long videos across diverse content. At Kuaishou, LPM processes videos accounting for approximately 45% of total viewing time, consistently improving quality-of-experience metrics. Furthermore, it reduces bitrate by 20% relative to Kuaishou's in-house codec, yielding annual bandwidth cost savings on the order of hundreds of millions, and has been integrated into products like Kling.

Key takeaway

For MLOps engineers optimizing video delivery on large UGC platforms, LPM demonstrates that generative video restoration is production-ready. You should evaluate diffusion-based models for enhancing video quality and achieving substantial bitrate reductions, potentially saving hundreds of millions in annual bandwidth costs. Consider integrating such models into your processing pipelines to improve user experience and system efficiency.

Key insights

Industrial-scale generative video restoration is now practical, scalable, and cost-effective for diverse content.

Principles

• A unified system for data, training, and inference handles diverse UGC degradations.
• Generative models offer significant system-level benefits beyond perceptual quality.

Method

LPM employs an enhanced architecture, progressive training strategy, and temporal-pyramid inference for consistent, high-fidelity restoration of arbitrarily long videos.

In practice

• Deploy generative models for large-scale video restoration on UGC platforms.
• Integrate generative restoration into video pipelines for bitrate reduction.

Topics

• Generative Video Restoration
• Diffusion Models
• Large Processing Model
• Kuaishou
• User-Generated Content
• Bitrate Reduction

Best for: CTO, Executive, Computer Vision Engineer, AI Scientist, MLOps Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.