MythPose: Enhanced Detection of Complex Poses in Thangka Figures

Thangka is a unique form of painting in Tibet, which holds rich cultural significance and artistic value. In Thangkas, in addition to the standard human form, there are also figures with multiple limbs. Existing human pose estimation methods are not well suited for keypoint detection of figures in Thangka paintings. This paper builds upon YOLOv11-Pose and introduces the Mamba structure to enhance the model's ability to capture global features. A feature fusion module is employed to integrate both shallow and deep features, and a KAL loss function is proposed to alleviate the interference between keypoints of different body parts. In this study, a dataset of 6208 Thangka images is collected and annotated for Thangka keypoint detection, and data augmentation techniques are used to enhance the generalization of the dataset. Experimental results show that MythPose achieves 89.13% mAP@0.5, 92.51% PCK, and 87.22% OKS in human pose estimation tasks on Thangka images, outperforming the baseline model. This research not only provides a reference for the digital preservation of Thangka art but also offers insights for pose estimation tasks in other similar artworks.