{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepfuse-an-imu-aware-network-for-real-time","title":"DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image","arxiv_id":"1912.04071","date":"2019-12-09","proceeding":null,"authors":["Fuyang Huang","Ailing Zeng","Minhao Liu","Qiuxia Lai","Qiang Xu"],"abstract":"In this paper, we propose a two-stage fully 3D network, namely \\textbf{DeepFuse}, to estimate human pose in 3D space by fusing body-worn Inertial Measurement Unit (IMU) data and multi-view images deeply. The first stage is designed for pure vision estimation. To preserve data primitiveness of multi-view inputs, the vision stage uses multi-channel volume as data representation and 3D soft-argmax as activation layer. The second one is the IMU refinement stage which introduces an IMU-bone layer to fuse the IMU and vision data earlier at data level. without requiring a given skeleton model a priori, we can achieve a mean joint error of $28.9$mm on TotalCapture dataset and $13.4$mm on Human3.6M dataset under protocol 1, improving the SOTA result by a large margin. Finally, we discuss the effectiveness of a fully 3D network for 3D pose estimation experimentally which may benefit future research.","url_abs":"https://arxiv.org/abs/1912.04071v1","url_pdf":"https://arxiv.org/pdf/1912.04071v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"3d-pose-estimation","task_name":"3D Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-human-pose-estimation-on-total-capture","task":"3D Human Pose Estimation","dataset":"Total Capture","model":"DeepFuse-IMU","rank_in_archive_order":5,"of":14,"metrics":{"Average MPJPE (mm)":"28.9"},"uses_additional_data":false},{"leaderboard":"/sota/3d-human-pose-estimation-on-total-capture","task":"3D Human Pose Estimation","dataset":"Total Capture","model":"DeepFuse-Vision Only","rank_in_archive_order":8,"of":14,"metrics":{"Average MPJPE (mm)":"32.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1912.04071","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}