A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image

ICCV 2019 Fu XiongBoshen ZhangYang XiaoZhiguo CaoTaidong YuJoey Tianyi ZhouJunsong 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... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Hand Pose Estimation HANDS 2017 A2J Average 3D Error 8.57 # 1
Hand Pose Estimation ICVL Hands A2J Average 3D Error 6.461 # 3
FPS 105.06 # 1
Pose Estimation ITOP front-view A2J Mean mAP 88.0 # 1
3D Pose Estimation K2HPD A2J FPS 93.78 # 1
Hand Pose Estimation K2HPD A2J [email protected] 76.3 # 1
Depth Estimation NYU-Depth V2 A2J mAP 8.61 # 1
Hand Pose Estimation NYU Hands A2J Average 3D Error 8.61 # 3
FPS 105.06 # 1

Methods used in the Paper