2021/08/10 by Ben Usman, Usman, Ben, Andrea Tagliasacchi +5 · 1 citation
Computer Science · Medicine · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #Diabetic Foot Ulcer Assessment and Management #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2108.04869
openalex publication_date 2021/08/10 · arxiv created 2021/11/25 · arxiv updated 2021/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the era of deep learning, human pose estimation from multiple cameras with unknown calibration has received little attention to date. We show how to train a neural model to perform this task with high precision and minimal latency overhead. The proposed model takes into account joint location uncertainty due to occlusion from multiple views, and requires only 2D keypoint data for training. Our method outperforms both classical bundle adjustment and weakly-supervised monocular 3D baselines on the well-established Human3.6M dataset, as well as the more challenging in-the-wild Ski-Pose PTZ dataset.