ILSH: The Imperial Light-Stage Head Dataset for Human Head View Synthesis

• URL: http://arxiv.org/abs/2310.03952v1
• Date: Fri, 6 Oct 2023 00:32:36 GMT
• Title: ILSH: The Imperial Light-Stage Head Dataset for Human Head View Synthesis
• Authors: Jiali Zheng, Youngkyoon Jang, Athanasios Papaioannou, Christos Kampouris, Rolandos Alexandros Potamias, Foivos Paraperas Papantoniou, Efstathios Galanakis, Ales Leonardis, Stefanos Zafeiriou
• Abstract summary: Imperial Light-Stage Head dataset is a novel dataset designed to support view synthesis academic challenges for human heads. This paper details the setup of a light-stage specifically designed to capture high-resolution (4K) human head images. In addition to the data collection, we address the split of the dataset into train, validation, and test sets.
• Score: 42.81410101705251
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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