AI RESEARCH
Expanding mmWave Datasets for Human Pose Estimation with Unlabeled Data and LiDAR Datasets
arXiv CS.CV
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ArXi:2603.14507v1 Announce Type: new Current mmWave datasets for human pose estimation (HPE) are scarce and lack diversity in both point cloud (PC) attributes and human poses, severely hampering the generalization ability of their trained models. On the other hand, unlabeled mmWave HPE data and diverse LiDAR HPE datasets are readily available. We propose EMDUL, a novel approach to expand the volume and diversity of an existing mmWave dataset using unlabeled mmWave data and a LiDAR dataset.