Papers › OriNet: A Fully Convolutional Network for 3D Human Pose Estimation
OriNet: A Fully Convolutional Network for 3D Human Pose Estimation
Chenxu Luo, Xiao Chu, Alan Yuille
In this paper, we propose a fully convolutional network for 3D human pose estimation from monocular images. We use limb orientations as a new way to represent 3D poses and bind the orientation together with the bounding box of each limb region to better associate images and predictions. The 3D orientations are modeled jointly with 2D keypoint detections. Without additional constraints, this simple method can achieve good results on several large-scale benchmarks. Further experiments show that our method can generalize well to novel scenes and is robust to inaccurate bounding boxes.
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Code
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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 | MPI-INF-3DHP | OriNet | AUC | 32.1 | #102 of 108 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | OriNet | PCK | 64.6 | #102 of 108 | Archive leaderboard | report |
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