ILSH: The Imperial Light-Stage Head Dataset for Human Head View
  Synthesis
Jiali Zheng Youngkyoon Jang Athanasios Papaioannou Christos Kampouris Rolandos Alexandros Potamias Foivos Paraperas Papantoniou Efstathios Galanakis Ales Leonardis Stefanos Zafeiriou
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.