Throughout the history of art, the pose, as the holistic abstraction of the human body's expression, has proven to be a constant in numerous studies. However, due to the enormous amount of data that so far had to be processed by hand, its crucial role to the formulaic recapitulation of art-historical motifs since antiquity could only be highlighted selectively. This is true even for the now automated estimation of human poses, as domain-specific, sufficiently large data sets required for training computational models are either not publicly available or not indexed at a fine enough granularity. With the Poses of People in Art data set, we introduce the first openly licensed data set for estimating human poses in art and validating human pose estimators. It consists of 2,454 images from 22 art-historical depiction styles, including those that have increasingly turned away from lifelike representations of the body since the 19th century. A total of 10,749 human figures are precisely enclosed by rectangular bounding boxes, with a maximum of four per image labeled by up to 17 keypoints; among these are mainly joints such as elbows and knees. For machine learning purposes, the data set is divided into three subsets, training, validation, and testing, that follow the established JSON-based Microsoft COCO format, respectively. Each image annotation, in addition to mandatory fields, provides metadata from the art-historical online encyclopedia WikiArt. With this paper, we elaborate on the acquisition and constitution of the data set, address various application scenarios, and discuss prospects for a digitally supported art history. We show that the data set enables the investigation of body phenomena in art, whether at the level of individual figures, which can be captured in their subtleties, or entire figure constellations, whose position, distance, or proximity to one another is considered.
翻译:在整个艺术史上,由于人体表达的整体抽象化,这种结构在众多的研究中被证明是一个固定不变的。然而,由于迄今为止需要手工处理的大量数据,因此它对于对艺术历史形态的公式总结具有关键作用,因为古老只能有选择地加以强调。即使是目前对人体形态的自动估计,因为对域而言,培训计算模型所需要的足够大的数据组要么没有公开提供,要么没有以微小的颗粒进行索引。随着艺术数据集中的人的情景,我们引入了第一个公开许可的数据组,用于在艺术中估计人姿势和验证人类姿势的图案。它由22种艺术历史形态的2,454个图像组成,包括自19世纪以来日益脱离对人体形态的像生命一样的图案。总共10,749个人类数字组完全由矩形捆绑框组成,最多4个图像组标有17个关键点的图案。我们这些图案组中的第一个公开许可数据集数据集集用于在艺术中估算人造型结构, 并且主要用于在线数据组的预估测,每个数字组, 以及每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每部、每