POLAR: A Portrait OLAT Dataset and Generative Framework for Illumination-Aware Face Modeling
This work addresses the data scarcity issue in portrait illumination modeling for computer vision and graphics applications, representing a novel framework rather than an incremental improvement.
The authors tackled the problem of limited illumination data for face relighting by introducing POLAR, a large-scale OLAT dataset with over 200 subjects under 156 lighting directions, and POLARNet, a flow-based generative model that predicts per-light responses from a single portrait, enabling scalable and controllable relighting.
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/.