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3D Multi Person Pose Estimation Absolute On

Metrics

3DPCK

Results

Performance results of various models on this benchmark

Model Name
3DPCK
Paper TitleRepository
TDBU_Net48.0Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks
WDSPose37.3Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision
PIRN44.1Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation-
GnTCN45.7Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos
HMOR43.8HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation-
GR-M3D41.2Dynamic Graph Reasoning for Multi-person 3D Pose Estimation-
Liu et al.36.5Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation-
Depth Prediction Network-Absolute Human Pose Estimation with Depth Prediction Network
HDNet35.2HDNet: Human Depth Estimation for Multi-Person Camera-Space Localization
VirtualPose44VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data
3DMPPE_POSENET31.5Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
SMAP35.4SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation
POTR-3D50.9Towards Robust and Smooth 3D Multi-Person Pose Estimation from Monocular Videos in the Wild-
DAS 39.2Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation
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