  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.
