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.

翻译：人体姿态估计（HPE）是理解艺术作品（如希腊花瓶绘画）中人物视觉叙事和身体动作的核心环节。然而，现有HPE方法在跨域场景下泛化能力不足，导致姿态识别效果较差。为此，我们提出两阶段方法：（1）通过图像风格迁移技术，将包含已知人物和姿态标注的自然图像数据集适配至希腊花瓶绘画风格。我们引入基于感知一致性的风格迁移训练以强化感知一致性，随后使用新生成的数据集对基础模型进行微调。实验表明，采用风格迁移学习后，模型在未标注数据上的平均精度（mAP）和平均召回率（mAR）均显著超越当前最优方法（SOTA），提升幅度超过6%。（2）为进一步优化已取得的优异结果，我们构建了一个小型数据集（ClassArch），包含公元前6-5世纪古希腊花瓶绘画中的人物及姿态标注。研究显示，使用该数据集对经过风格迁移预训练的模型进行微调可获得更优性能。通过全面的消融实验，我们针对风格强度的影响进行定向分析，揭示模型具备学习通用域风格的能力。此外，我们构建了基于姿态的图像检索系统，验证了方法的有效性。