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3D Multi Person Pose Estimation Absolute On

Métriques

3DPCK

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	3DPCK	Paper Title	Repository
TDBU_Net	48.0	Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks
WDSPose	37.3	Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision
PIRN	44.1	Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation	-
GnTCN	45.7	Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos
HMOR	43.8	HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation	-
GR-M3D	41.2	Dynamic Graph Reasoning for Multi-person 3D Pose Estimation	-
Liu et al.	36.5	Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation	-
Depth Prediction Network	-	Absolute Human Pose Estimation with Depth Prediction Network
HDNet	35.2	HDNet: Human Depth Estimation for Multi-Person Camera-Space Localization
VirtualPose	44	VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data
3DMPPE_POSENET	31.5	Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
SMAP	35.4	SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation
POTR-3D	50.9	Towards Robust and Smooth 3D Multi-Person Pose Estimation from Monocular Videos in the Wild	-
DAS	39.2	Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation

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