Recent single-image relighting methods, powered by advanced generative models, have achieved impressive photorealism on synthetic benchmarks. However, their effectiveness in the complex visual landscape of the real world remains largely unverified. A critical gap exists, as current datasets are typically designed for multi-view reconstruction and fail to address the unique challenges of single-image relighting. To bridge this synthetic-to-real gap, we introduce WildRelight, the first in-the-wild dataset specifically created for evaluating single-image relighting models. WildRelight features a diverse collection of high-resolution outdoor scenes, captured under strictly aligned, temporally varying natural illuminations, each paired with a high-dynamic-range environment map. Using this data, we establish a rigorous benchmark revealing that state-of-the-art models trained on synthetic data suffer from severe domain shifts. The strictly aligned temporal structure of WildRelight enables a new paradigm for domain adaptation. We demonstrate this by introducing a physics-guided inference framework that leverages the captured natural light evolution as a self-supervised constraint. By integrating Diffusion Posterior Sampling (DPS) with temporal Sampling-Aware Test-Time Adaptation (TTA), we show that the dataset allows synthetic models to align with real-world statistics on-the-fly, transforming the intractable sim-to-real challenge into a tractable self-supervised task. The dataset and code will be made publicly available to foster robust, physically-grounded relighting research.

翻译：近期，基于先进生成模型的单图像重照明方法在合成基准上实现了令人印象深刻的照片级真实感。然而，这些方法在复杂现实世界视觉环境中的有效性仍未得到充分验证。当前数据集通常针对多视角重建设计，未能解决单图像重照明的独特挑战，因此存在关键性空白。为弥合这一合成到现实（sim-to-real）的鸿沟，我们提出了WildRelight——首个专为评估单图像重照明模型而构建的真实野外数据集。WildRelight包含大量高分辨率室外场景，这些场景在严格对齐、随时间变化的自然光照条件下采集，并配有高动态范围环境贴图。利用该数据，我们建立了严格的基准测试，揭示出在合成数据上训练的先进模型存在严重的域偏移。WildRelight严格对齐的时间结构为域自适应提供了新范式。我们通过引入一个物理引导的推理框架来展示这一能力，该框架将捕获的自然光演化作为自监督约束。通过将扩散后验采样（DPS）与时间感知采样测试时自适应（TTA）相结合，我们证明了该数据集可让合成模型实时对齐现实世界的统计特性，从而将原本棘手的仿真到现实挑战转化为可处理的自监督任务。我们将在公开数据集和代码，以推动稳健的、具有物理基础的重照明研究。