Papers › DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image
DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image
Fuyang Huang, Ailing Zeng, Minhao Liu, Qiuxia Lai, Qiang Xu
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Human Pose Estimation | Total Capture | DeepFuse-IMU | Average MPJPE (mm) | 28.9 | #5 of 14 | Archive leaderboard | report |
| 3D Human Pose Estimation | Total Capture | DeepFuse-Vision Only | Average MPJPE (mm) | 32.7 | #8 of 14 | Archive leaderboard | report |
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