首个全身人像光照与新视角合成的大规模数据集，推动数字人渲染研究。
HumanOLAT: A Large-Scale Dataset for Full-Body Human Relighting and Novel-View Synthesis
- 采集全身人物在多种光照下的多视角图像，支持逐光渲染。
- 包含白光、环境贴图、色阶及精细逐光光照数据，覆盖真实复杂光照。
- 适合研究人体外观建模、光照重建与三维人像渲染的学者使用。
数字人像的同步光照重置与新视角生成是一项重要但极具挑战的任务，应用广泛。该任务进展受限于缺乏公开可用的高质量数据集，尤其在全身人像捕获方面。为此，我们推出 HumanOLAT 数据集，这是首个公开的大规模多视角一光一照（OLAT）全身人像数据集。数据集包含在多种光照条件下的高动态范围（HDR）RGB 帧，包括白光、环境贴图、颜色梯度以及精细的 OLAT 光照。对现有先进重光照与新视角合成方法的评估表明，该数据集具有重要价值，同时也揭示了建模复杂人体-光照交互仍面临巨大挑战。我们相信 HumanOLAT 将显著促进未来研究，为通用与人像专用重光照及渲染技术提供严谨的基准测试平台。
原文摘要 · Abstract (English)
Simultaneous relighting and novel-view rendering of digital human representations is an important yet challenging task with numerous applications. Progress in this area has been significantly limited due to the lack of publicly available, high-quality datasets, especially for full-body human captures. To address this critical gap, we introduce the HumanOLAT dataset, the first publicly accessible large-scale dataset of multi-view One-Light-at-a-Time (OLAT) captures of full-body humans. The dataset includes HDR RGB frames under various illuminations, such as white light, environment maps, color gradients and fine-grained OLAT illuminations. Our evaluations of state-of-the-art relighting and novel-view synthesis methods underscore both the dataset's value and the significant challenges still present in modeling complex human-centric appearance and lighting interactions. We believe HumanOLAT will significantly facilitate future research, enabling rigorous benchmarking and advancements in both general and human-specific relighting and rendering techniques.
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