解决视频重打光失真问题，实现高保真高清重打光。
Hi-Light: A Path to high-fidelity, high-resolution video relighting with a Novel Evaluation Paradigm
- 基于光照先验的扩散模型稳定中间结果
- 新指标衡量光照一致性，优于现有方法
- 适合影视特效与高质量视频编辑人群
视频重打光具有巨大的创作和商业价值，但受限于缺乏有效评估指标、严重光照闪烁以及细节退化等问题。为此，我们提出无需训练的Hi-Light框架，实现高保真、高分辨率、鲁棒的视频重打光。创新包括：基于光照先验的引导扩散模型以稳定中间重打光视频；融合光流的混合运动自适应光照平滑滤波器，在不引入运动模糊的前提下保障时序稳定性；基于LAB空间的细节融合模块，保留原始视频中的高频细节信息。此外，为填补评估空白，我们提出首个专门衡量光照一致性的定量指标——光照稳定性得分（Light Stability Score）。大量实验表明，Hi-Light在定性和定量对比中均显著优于当前最优方法，生成稳定且细节丰富的重打光视频。
原文摘要 · Abstract (English)
Video relighting offers immense creative potential and commercial value but is hindered by challenges, including the absence of an adequate evaluation metric, severe light flickering, and the degradation of fine-grained details during editing. To overcome these challenges, we introduce Hi-Light, a novel, training-free framework for high-fidelity, high-resolution, robust video relighting. Our approach introduces three technical innovations: lightness prior anchored guided relighting diffusion that stabilises intermediate relit video, a Hybrid Motion-Adaptive Lighting Smoothing Filter that leverages optical flow to ensure temporal stability without introducing motion blur, and a LAB-based Detail Fusion module that preserves high-frequency detail information from the original video. Furthermore, to address the critical gap in evaluation, we propose the Light Stability Score, the first quantitative metric designed to specifically measure lighting consistency. Extensive experiments demonstrate that Hi-Light significantly outperforms state-of-the-art methods in both qualitative and quantitative comparisons, producing stable, highly detailed relit videos.
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