High-Fidelity Synthetic MetaAcuPoint Depth (MAP-d) Dataset for Acupoint Localization Using MetaHuman Avatars

Abstract

Manual annotation of anatomical landmarks, such as acupuncture points or acupoints, is labor-intensive, prone to variability, and limited in clinical datasets. To overcome these constraints, this study introduces the MetaAcuPoint depth dataset (MAP-d dataset), a novel high-fidelity synthetic RGB-D dataset generated using Unreal Engine 5.4 and Epic Games’ MetaHuman avatars. The dataset provides pixel-aligned RGB-D image pairs with anatomically consistent annotations for five clinically relevant acupoints: LI4, TE3, TE5, LI10, and LI11. Reusable skeletal sockets ensure annotation reproducibility across diverse hand morphologies. Domain randomization in pose, skin tone, and skeletal structure enhances the dataset’s demographic and anatomical variability. A lightweight convolutional neural network benchmarked the dataset using RGB-only and RGB-D inputs. While absolute localization accuracy remains modest due to the model’s simplicity, depth augmentation consistently improved performance, yielding a 22.9% reduction in mean distance error and a 57.6% increase in PCK@10. These results confirm the effectiveness of the dataset design. MAP-d provides a scalable resource for developing acupoint localization models, with future potential in clinical training, augmented reality, and real-world deployment.