RRNet实现实时视频增强，精准调控局部光照。
RRNet: Configurable Real-Time Video Enhancement with Arbitrary Local Lighting Variations
- 通过虚拟光源参数估计实现局部重光照，无需像素对齐数据。
- 在低光、局部光照不均场景下，显著优于现有方法。
- 轻量架构支持实时高分辨率运行，适合会议/AR/手机摄影。
随着实时视频增强在直播应用中的需求增长，现有方法在速度与有效曝光控制之间难以平衡，尤其在光照不均情况下表现不佳。本文提出轻量且可配置的RRNet（Rendering Relighting Network）框架，在视觉质量与效率间实现当前最优权衡。通过估计一组最小虚拟光源参数，结合深度感知渲染模块，实现无需像素对齐训练数据的局部重光照。该对象感知设计保留人脸身份，配合精简编码器与轻量预测头，支持实时高分辨率性能。为促进训练，我们提出基于生成式AI的数据集构建流程，低成本合成多样化光照条件。凭借可解释的光照控制与高效架构，RRNet适用于视频会议、AR人像增强及移动摄影等实际场景。实验表明，其在低光增强、局部光照调节与眩光去除方面持续优于先前方法。
原文摘要 · Abstract (English)
With the growing demand for real-time video enhancement in live applications, existing methods often struggle to balance speed and effective exposure control, particularly under uneven lighting. We introduce RRNet (Rendering Relighting Network), a lightweight and configurable framework that achieves a state-of-the-art tradeoff between visual quality and efficiency. By estimating parameters for a minimal set of virtual light sources, RRNet enables localized relighting through a depth-aware rendering module without requiring pixel-aligned training data. This object-aware formulation preserves facial identity and supports real-time, high-resolution performance using a streamlined encoder and lightweight prediction head. To facilitate training, we propose a generative AI-based dataset creation pipeline that synthesizes diverse lighting conditions at low cost. With its interpretable lighting control and efficient architecture, RRNet is well suited for practical applications such as video conferencing, AR-based portrait enhancement, and mobile photography. Experiments show that RRNet consistently outperforms prior methods in low-light enhancement, localized illumination adjustment, and glare removal.
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