打造真实补光数据集与模型，让人脸补光更自然且不破坏背景光照。
Light Up Your Face: A Physically Consistent Dataset and Diffusion Model for Face Fill-Light Enhancement
- 用物理一致渲染生成16万组人脸补光前后数据，补光参数可拆解控制。
- 提出单步扩散模型FiLitDiff，补光效果逼真且计算成本低。
- 适合需要精准控制补光、避免背景失真的图像修复研究者使用。
人脸补光增强（FFE）通过添加虚拟补光来照亮过暗的人脸，同时保持原始场景光照和背景不变。现有方法多聚焦整体光照重塑，易抑制输入光照或改变整个场景，导致前景与背景不一致，难以满足实际需求。为支持规模化学习，我们构建了大规模成对数据集LightYourFace-160K（LYF-160K），基于物理一致渲染器注入由六个解耦因素控制的圆形补光区域，生成16万组前后对比样本。我们首先预训练一个物理感知光照提示（PALP），将6维参数嵌入条件标记，并引入辅助平面光重建任务。在此基础上，基于预训练扩散主干网络，训练出一种补光扩散模型FiLitDiff，该模型以物理基础光照代码为条件，实现高效、可控、高保真的单步补光，计算开销极低。在独立测试集上的实验表明，该方法在感知质量与全参考指标上表现优异，且更有效保留背景光照。数据集与模型已开源：https://github.com/gobunu/Light-Up-Your-Face。
原文摘要 · Abstract (English)
Face fill-light enhancement (FFE) brightens underexposed faces by adding virtual fill light while keeping the original scene illumination and background unchanged. Most face relighting methods aim to reshape overall lighting, which can suppress the input illumination or modify the entire scene, leading to foreground-background inconsistency and mismatching practical FFE needs. To support scalable learning, we introduce LightYourFace-160K (LYF-160K), a large-scale paired dataset built with a physically consistent renderer that injects a disk-shaped area fill light controlled by six disentangled factors, producing 160K before-and-after pairs. We first pretrain a physics-aware lighting prompt (PALP) that embeds the 6D parameters into conditioning tokens, using an auxiliary planar-light reconstruction objective. Building on a pretrained diffusion backbone, we then train a fill-light diffusion (FiLitDiff), an efficient one-step model conditioned on physically grounded lighting codes, enabling controllable and high-fidelity fill lighting at low computational cost. Experiments on held-out paired sets demonstrate strong perceptual quality and competitive full-reference metrics, while better preserving background illumination. The dataset and model will be at https://github.com/gobunu/Light-Up-Your-Face.
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