POLAR: A Portrait OLAT Dataset and Generative Framework for Illumination-Aware Face Modeling

• URL: http://arxiv.org/abs/2512.13192v2
• Date: Tue, 16 Dec 2025 03:46:33 GMT
• Title: POLAR: A Portrait OLAT Dataset and Generative Framework for Illumination-Aware Face Modeling
• Authors: Zhuo Chen, Chengqun Yang, Zhuo Su, Zheng Lv, Jingnan Gao, Xiaoyuan Zhang, Xiaokang Yang, Yichao Yan,
• Abstract summary: Face relighting aims to synthesize realistic portraits under novel illumination while preserving identity and geometry.<n>We introduce POLAR, a large-scale and physically calibrated One-Light-at-a-Time dataset containing over 200 subjects captured under 156 lighting directions, multiple views, and diverse expressions.<n>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.
• Score: 51.7495375918484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Our project page: https://rex0191.github.io/POLAR/.

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