Radar-based human pose estimation has focused on improving learning algorithms while representing the body as unconstrained keypoint coordinates. We address the underexplored dimension of anatomical fidelity by integrating a full-body skeletal model into a differentiable, end-to-end trainable radar-based pose estimation framework, in which the pose network is supervised through forward kinematics while subject-specific geometry is fitted beforehand.
arXiv:2609.34768v2 Announce Type: replace
Abstract: Millimeter-wave (mmWave) radar enables privacy-preserving human perception, but the extreme sparsity of point clouds from commercial single-chip se...
By Shuxing Zhang, Yongquan Ni, Zhenyu Ding, Yawen Lin
arXiv:2608.29928v1 Announce Type: new
Abstract: State-of-the-art monocular body recovery methods predict mesh vertices and angles on the corresponding kinematic tree, but their outputs lack biomechan...
By R. James Cotton, J. D. Peiffer, Lucinda Williamson, John Leske, Georgios Pavlakos
arXiv:2606. 12988v1 Announce Type: cross Abstract: This paper introduces a new methodology for real-time prediction of ergonomic and non-ergonomic human poses using volumetric video data in three dimensions.
By Manex Atxa, Bruno Simoes, Julen Balzategui
arXiv:2506. 17332v2 Announce Type: replace-cross Abstract: By 2050, people aged 65 and over are projected to make up 16% of the global population.
By Haitian Wang, Yiren Wang, Xinyu Wang, Yumeng Miao, Yuliang Zhang, Yu Zhang, Atif Mansoor
arXiv:2606. 00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings.
By Flavio Di Martino, Mattia G. Campana, Marcello Magno, Lorenza Pratali, Franca Delmastro