POLAR: A Portrait OLAT Dataset and Generative Framework for Illumination-Aware Face Modeling
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908714299031552 |
|---|---|
| author | Chen, Zhuo Yang, Chengqun Su, Zhuo Lv, Zheng Gao, Jingnan Zhang, Xiaoyuan Yang, Xiaokang Yan, Yichao |
| author_facet | Chen, Zhuo Yang, Chengqun Su, Zhuo Lv, Zheng Gao, Jingnan Zhang, Xiaoyuan Yang, Xiaokang Yan, Yichao |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | POLAR: A Portrait OLAT Dataset and Generative Framework for Illumination-Aware Face Modeling Chen, Zhuo Yang, Chengqun Su, Zhuo Lv, Zheng Gao, Jingnan Zhang, Xiaoyuan Yang, Xiaokang Yan, Yichao Computer Vision and Pattern Recognition 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/. |
| title | POLAR: A Portrait OLAT Dataset and Generative Framework for Illumination-Aware Face Modeling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.13192 |