# Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer Learning

arXiv: https://arxiv.org/abs/2012.05616 · HTML: https://mlsanity.com/paper/2012.05616/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 13 of 13)

- [Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes](https://mlsanity.com/paper/2303.02760/) (2023)
- [HumanRefiner: Benchmarking Abnormal Human Generation and Refining with Coarse-to-fine Pose-Reversible Guidance](https://mlsanity.com/paper/2407.06937/) (2024)
- [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)
- [SniffyArt: The Dataset of Smelling Persons](https://mlsanity.com/paper/2311.11888/) (2023)
- [ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints](https://mlsanity.com/paper/2210.09204/) (2022)
- [Transfer Learning for Olfactory Object Detection](https://mlsanity.com/paper/2301.09906/) (2023)
- [Automatic Analysis of Human Body Representations in Western Art](https://mlsanity.com/paper/2210.08860/) (2022)
- [Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset](https://mlsanity.com/paper/2507.08384/) (2025)
- [Sketch2PoseNet: Efficient and Generalized Sketch to 3D Human Pose Prediction](https://mlsanity.com/paper/2510.26196/) (2025)
- [Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey](https://mlsanity.com/paper/2412.01450/) (2024)
- [There Is a Digital Art History](https://mlsanity.com/paper/2308.07464/) (2023)
- [BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks](https://mlsanity.com/paper/2507.07134/) (2025)

## Abstract (arXiv)

Human pose estimation (HPE) is a central part of understanding the visual narration and body movements of characters depicted in artwork collections, such as Greek vase paintings. Unfortunately, existing HPE methods do not generalise well across domains resulting in poorly recognized poses. Therefore, we propose a two step approach: (1) adapting a dataset of natural images of known person and pose annotations to the style of Greek vase paintings by means of image style-transfer. We introduce a perceptually-grounded style transfer training to enforce perceptual consistency. Then, we fine-tune the base model with this newly created dataset. We show that using style-transfer learning significantly improves the SOTA performance on unlabelled data by more than 6% mean average precision (mAP) as well as mean average recall (mAR). (2) To improve the already strong results further, we created a small dataset (ClassArch) consisting of ancient Greek vase paintings from the 6-5th century BCE with person and pose annotations. We show that fine-tuning on this data with a style-transferred model improves the performance further. In a thorough ablation study, we give a targeted analysis of the influence of style intensities, revealing that the model learns generic domain styles. Additionally, we provide a pose-based image retrieval to demonstrate the effectiveness of our method.