FaceProbe: Recovering HDR Environment Map via Masked Diffusion and Physical Preference

Recovering $360^{\circ }$ HDR environment maps from unconstrained portraits is a fundamental yet ill-posed challenge in inverse rendering. Existing approaches typically suffer from a critical trade-off: traditional facial reflection-based methods yield low-fidelity, blurry results that lack practical utility, while recent generative models often resort to unconstrained panoramic hallucination (i.e., synthesizing environments with no physical grounding), which leads to severe overfitting and inaccurate lighting. In this paper, we present FaceProbe, an inpainting-driven framework that treats the human face as a reliable, physically-grounded light probe. Instead of synthesizing the environment from scratch, we reformulate the task as a masked completion and outpainting problem. By preserving the peripheral context of the input image and utilizing the portrait as a structural condition, our architecture leverages the generative priors of Diffusion Transformers (DiT) to extend sparse observations into high-fidelity, sharp $360^{\circ }$ HDR panoramas. To further eliminate color shifts and highlight inaccuracies, we introduce OLAT-GRPO, a post-training alignment mechanism based on Group Relative Policy Optimization. By subjecting the generated maps to image-based relighting on One-Light-At-a-Time (OLAT) datasets, we evaluate their physical validity through relighting consistency. This allows us to explicitly prune inconsistent denoising paths and align the model with multi-objective rewards, ensuring energy conservation and spectral accuracy. Extensive evaluations demonstrate that FaceProbe achieves a paradigm-shifting improvement. Most notably, in the critical portrait relighting task, our method reduces estimation errors by 54.4% in Angular Error and 48.5% in si-RMSE over state-of-the-art methods, delivering a robust and highly usable solution for photorealistic rendering and world model applications.