This paper introduces the Imperial Light-Stage Head (ILSH) dataset, a novel light-stage-captured human head dataset designed to support view synthesis academic challenges for human heads. The ILSH dataset is intended to facilitate diverse approaches, such as scene-specific or generic neural rendering, multiple-view geometry, 3D vision, and computer graphics, to further advance the development of photo-realistic human avatars. This paper details the setup of a light-stage specifically designed to capture high-resolution (4K) human head images and describes the process of addressing challenges (preprocessing, ethical issues) in collecting high-quality data. In addition to the data collection, we address the split of the dataset into train, validation, and test sets. Our goal is to design and support a fair view synthesis challenge task for this novel dataset, such that a similar level of performance can be maintained and expected when using the test set, as when using the validation set. The ILSH dataset consists of 52 subjects captured using 24 cameras with all 82 lighting sources turned on, resulting in a total of 1,248 close-up head images, border masks, and camera pose pairs.

翻译：本文介绍了一种全新光舞台捕捉的人头数据集——帝国光舞台人头（ILSH）数据集，该数据集专为支持人头视角合成学术挑战而设计。ILSH数据集旨在促进场景特定或通用神经渲染、多视图几何、三维视觉及计算机图形学等多样化方法的发展，以进一步推动照片级逼真人形虚拟形象的进步。本文详细描述了专为捕捉高分辨率（4K）人头图像而设计的光舞台装置，并阐述了在收集高质量数据过程中应对挑战（预处理、伦理问题）的方法。除数据采集外，我们还讨论了数据集划分为训练集、验证集和测试集的问题。我们的目标是为这一新型数据集设计并支持公平的视角合成挑战任务，使得使用测试集时能够保持并预期与验证集相似的性能水平。ILSH数据集包含52位受试者，通过24台相机在全部82个光源开启状态下采集，共计获得1248张近景人头图像、边界掩码及相机位姿对。