Papers › A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a...
A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image
Fu Xiong, Boshen Zhang, Yang Xiao, Zhiguo Cao, Taidong Yu, Joey Tianyi Zhou, Junsong Yuan
For 3D hand and body pose estimation task in depth image, a novel anchor-based approach termed Anchor-to-Joint regression network (A2J) with the end-to-end learning ability is proposed. Within A2J, anchor points able to capture global-local spatial context information are densely set on depth image as local regressors for the joints. They contribute to predict the positions of the joints in ensemble way to enhance generalization ability. The proposed 3D articulated pose estimation paradigm is different from the state-of-the-art encoder-decoder based FCN, 3D CNN and point-set based manners. To discover informative anchor points towards certain joint, anchor proposal procedure is also proposed for A2J. Meanwhile 2D CNN (i.e., ResNet-50) is used as backbone network to drive A2J, without using time-consuming 3D convolutional or deconvolutional layers. The experiments on 3 hand datasets and 2 body datasets verify A2J's superiority. Meanwhile, A2J is of high running speed around 100 FPS on single NVIDIA 1080Ti GPU.
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Pose Estimation | K2HPD | A2J | FPS | 93.78 | #1 of 1 | Archive leaderboard | report |
| Depth Estimation | NYU-Depth V2 | A2J | mAP | 8.61 | #17 of 17 | Archive leaderboard | report |
| Hand Pose Estimation | HANDS 2017 | A2J | Average 3D Error | 8.57 | #3 of 9 | Archive leaderboard | report |
| Hand Pose Estimation | ICVL Hands | A2J | Average 3D Error | 6.461 | #9 of 15 | Archive leaderboard | report |
| Hand Pose Estimation | ICVL Hands | A2J | FPS | 105.06 | #9 of 15 | Archive leaderboard | report |
| Hand Pose Estimation | K2HPD | A2J | PDJ@5mm | 76.3 | #1 of 1 | Archive leaderboard | report |
| Hand Pose Estimation | NYU Hands | A2J | Average 3D Error | 8.61 | #9 of 17 | Archive leaderboard | report |
| Hand Pose Estimation | NYU Hands | A2J | FPS | 105.06 | #9 of 17 | Archive leaderboard | report |
| Pose Estimation | ITOP front-view | A2J | Mean mAP | 88.0 | #5 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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