# Linking Art through Human Poses

arXiv: https://arxiv.org/abs/1907.03537 · HTML: https://mlsanity.com/paper/1907.03537/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 9 of 9)

- [Understanding and Creating Art with AI: Review and Outlook](https://mlsanity.com/paper/2102.09109/) (2021)
- [Iconographic Image Captioning for Artworks](https://mlsanity.com/paper/2102.03942/) (2021)
- [Semi-supervised Human Pose Estimation in Art-historical Images](https://mlsanity.com/paper/2207.02976/) (2022)
- [Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History](https://mlsanity.com/paper/2301.05124/) (2023)
- [Object Retrieval and Localization in Large Art Collections using Deep Multi-Style Feature Fusion and Iterative Voting](https://mlsanity.com/paper/2107.06935/) (2021)
- [Automatic Analysis of Human Body Representations in Western Art](https://mlsanity.com/paper/2210.08860/) (2022)
- [Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey](https://mlsanity.com/paper/2412.01450/) (2024)
- [ICC++: Explainable Image Retrieval for Art Historical Corpora using Image Composition Canvas](https://mlsanity.com/paper/2206.11115/) (2022)
- [AutoComPose: Automatic Generation of Pose Transition Descriptions for Composed Pose Retrieval Using Multimodal LLMs](https://mlsanity.com/paper/2503.22884/) (2025)

## Abstract (arXiv)

We address the discovery of composition transfer in artworks based on their visual content. Automated analysis of large art collections, which are growing as a result of art digitization among museums and galleries, is an important tool for art history and assists cultural heritage preservation. Modern image retrieval systems offer good performance on visually similar artworks, but fail in the cases of more abstract composition transfer. The proposed approach links artworks through a pose similarity of human figures depicted in images. Human figures are the subject of a large fraction of visual art from middle ages to modernity and their distinctive poses were often a source of inspiration among artists. The method consists of two steps -- fast pose matching and robust spatial verification. We experimentally show that explicit human pose matching is superior to standard content-based image retrieval methods on a manually annotated art composition transfer dataset.