Face relighting aims to synthesize realistic portraits under novel illumination while preserving identity and geometry. However, progress remains constrained by the limited availability of large-scale, physically consistent illumination data. To address this, we introduce POLAR, a large-scale and physically calibrated One-Light-at-a-Time (OLAT) dataset containing over 200 subjects captured under 156 lighting directions, multiple views, and diverse expressions. Building upon POLAR, we develop a flow-based generative model POLARNet that predicts per-light OLAT responses from a single portrait, capturing fine-grained and direction-aware illumination effects while preserving facial identity. Unlike diffusion or background-conditioned methods that rely on statistical or contextual cues, our formulation models illumination as a continuous, physically interpretable transformation between lighting states, enabling scalable and controllable relighting. Together, POLAR and POLARNet form a unified illumination learning framework that links real data, generative synthesis, and physically grounded relighting, establishing a self-sustaining "chicken-and-egg" cycle for scalable and reproducible portrait illumination.

翻译：人脸重光照旨在在保持身份与几何特征不变的前提下，合成新光照条件下的真实感肖像。然而，当前进展受限于大规模、物理一致的光照数据稀缺。为此，我们提出了POLAR——一个大规模且经过物理标定的单光源逐次照明（OLAT）数据集，包含超过200位被试者在156个光照方向、多视角及多种表情下的采集数据。基于POLAR，我们开发了基于流的生成模型POLARNet，该模型能够从单张肖像预测每光源的OLAT响应，在保持面部身份的同时捕捉细粒度、方向敏感的光照效应。与依赖统计特征或上下文线索的扩散模型或背景条件化方法不同，我们的建模框架将光照视为光照状态间连续、物理可解释的变换，从而实现可扩展且可控的重光照。POLAR与POLARNet共同构成了一个统一的光照学习框架，将真实数据、生成合成与物理基础的重光照技术相连接，为可扩展、可复现的肖像光照研究建立了自持的“鸡与蛋”循环体系。