# Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History

arXiv: https://arxiv.org/abs/2301.05124 · HTML: https://mlsanity.com/paper/2301.05124/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 103 references that are in the map.
- First posted on arXiv 12 Jan 2023.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [TOOD: Task-aligned One-stage Object Detection](https://mlsanity.com/paper/2108.07755/) (2021)
- [How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval](https://mlsanity.com/paper/1810.09617/) (2018)
- [The ArtBench Dataset: Benchmarking Generative Models with Artworks](https://mlsanity.com/paper/2206.11404/) (2022)
- [Prime Sample Attention in Object Detection](https://mlsanity.com/paper/1904.04821/) (2019)
- [BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography](https://mlsanity.com/paper/1704.08614/) (2017)
- [The Met Dataset: Instance-level Recognition for Artworks](https://mlsanity.com/paper/2202.01747/) (2022)
- [A Dataset and Baselines for Visual Question Answering on Art](https://mlsanity.com/paper/2008.12520/) (2020)
- [Side-Aware Boundary Localization for More Precise Object Detection](https://mlsanity.com/paper/1912.04260/) (2019)
- [Weakly Supervised Object Detection in Artworks](https://mlsanity.com/paper/1810.02569/) (2018)
- [Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning](https://mlsanity.com/paper/1903.02678/) (2019)
- [Detecting People in Artwork with CNNs](https://mlsanity.com/paper/1610.08871/) (2016)
- [A Data Set and a Convolutional Model for Iconography Classification in Paintings](https://mlsanity.com/paper/2010.11697/) (2020)
- [Iconographic Image Captioning for Artworks](https://mlsanity.com/paper/2102.03942/) (2021)
- [Improving Object Detection in Art Images Using Only Style Transfer](https://mlsanity.com/paper/2102.06529/) (2021)
- [Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer Learning](https://mlsanity.com/paper/2012.05616/) (2020)
- [Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts](https://mlsanity.com/paper/2008.01178/) (2020)
- [Linking Art through Human Poses](https://mlsanity.com/paper/1907.03537/) (2019)
- [Understanding Compositional Structures in Art Historical Images using Pose and Gaze Priors](https://mlsanity.com/paper/2009.03807/) (2020)
- [Towards Balanced Learning for Instance Recognition](https://mlsanity.com/paper/2108.10175/) (2021)
- [Multitask Painting Categorization by Deep Multibranch Neural Network](https://mlsanity.com/paper/1812.08052/) (2018)

## Built on by (most cited first, 6 of 6)

- [Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset](https://mlsanity.com/paper/2507.08384/) (2025)
- [On the Explainability of Vision-Language Models in Art History](https://mlsanity.com/paper/2602.20853/) (2026)
- [Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey](https://mlsanity.com/paper/2412.01450/) (2024)
- [Gesture Classification in Artworks Using Contextual Image Features](https://mlsanity.com/paper/2412.03456/) (2024)
- [Data Augmentation via Latent Diffusion Models for Detecting Smell-Related Objects in Historical Artworks](https://mlsanity.com/paper/2509.14755/) (2025)
- [Automated Goldsmith's Mark Retrieval in Silverware](https://mlsanity.com/paper/2609.20509/) (2026)

## Abstract (arXiv)

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.