{"ID":2864832,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23455","arxiv_id":"2509.23455","title":"3DPCNet: Pose Canonicalization for Robust Viewpoint-Invariant 3D Kinematic Analysis from Monocular RGB cameras","abstract":"Monocular 3D pose estimators produce camera-centered skeletons, creating view-dependent kinematic signals that complicate comparative analysis in applications such as health and sports science. We present 3DPCNet, a compact, estimator-agnostic module that operates directly on 3D joint coordinates to rectify any input pose into a consistent, body-centered canonical frame. Its hybrid encoder fuses local skeletal features from a graph convolutional network with global context from a transformer via a gated cross-attention mechanism. From this representation, the model predicts a continuous 6D rotation that is mapped to an $SO(3)$ matrix to align the pose. We train the model in a self-supervised manner on the MM-Fi dataset using synthetically rotated poses, guided by a composite loss ensuring both accurate rotation and pose reconstruction. On the MM-Fi benchmark, 3DPCNet reduces the mean rotation error from over 20$^{\\circ}$ to 3.4$^{\\circ}$ and the Mean Per Joint Position Error from ~64 mm to 47 mm compared to a geometric baseline. Qualitative evaluations on the TotalCapture dataset further demonstrate that our method produces acceleration signals from video that show strong visual correspondence to ground-truth IMU sensor data, confirming that our module removes viewpoint variability to enable physically plausible motion analysis.","short_abstract":"Monocular 3D pose estimators produce camera-centered skeletons, creating view-dependent kinematic signals that complicate comparative analysis in applications such as health and sports science. We present 3DPCNet, a compact, estimator-agnostic module that operates directly on 3D joint coordinates to rectify any input p...","url_abs":"https://arxiv.org/abs/2509.23455","url_pdf":"https://arxiv.org/pdf/2509.23455v1","authors":"[\"Tharindu Ekanayake\",\"Constantino Álvarez Casado\",\"Miguel Bordallo López\"]","published":"2025-09-27T18:55:21Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}