3D Multi-Person Pose Estimation
32 papers with code • 5 benchmarks • 4 datasets
This task aims to solve root-relative 3D multi-person pose estimation. No human bounding box and root joint coordinate groundtruth are used in testing time.
( Image credit: RootNet )
Benchmarks
These leaderboards are used to track progress in 3D Multi-Person Pose Estimation
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Libraries
Use these libraries to find 3D Multi-Person Pose Estimation models and implementations
Panoptic
MuPoTS-3D
AGORA
Store dataset
Subtasks
Latest papers
Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot
naver/multi-hmr
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We present Multi-HMR, a strong single-shot model for multi-person 3D human mesh recovery from a single RGB image.
Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild
mks0601/NeuralAnnot_RELEASE
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Recovering 3D human mesh in the wild is greatly challenging as in-the-wild (ITW) datasets provide only 2D pose ground truths (GTs).
Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement
To tackle the challenges, we propose a coarse-to-fine pipeline that benefits from 1) inverse kinematics from the occlusion-robust 3D skeleton estimation and 2) Transformer-based relation-aware refinement techniques.
AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression
With the proposed body representation, we further deliver a compact single-stage multi-person pose regression network, termed as AdaptivePose.
Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection
While the voxel-based methods have achieved promising results for multi-person 3D pose estimation from multi-cameras, they suffer from heavy computation burdens, especially for large scenes.
VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data
wkom/virtualpose
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While monocular 3D pose estimation seems to have achieved very accurate results on the public datasets, their generalization ability is largely overlooked.
Dual networks based 3D Multi-Person Pose Estimation from Monocular Video
3dpose/3D-Multi-Person-Pose
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Most of the methods focus on single persons, which estimate the poses in the person-centric coordinates, i. e., the coordinates based on the center of the target person.
Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation
In this paper, we present a novel Distribution-Aware Single-stage (DAS) model for tackling the challenging multi-person 3D pose estimation problem.
Direct Multi-view Multi-person 3D Pose Estimation
Instead of estimating 3D joint locations from costly volumetric representation or reconstructing the per-person 3D pose from multiple detected 2D poses as in previous methods, MvP directly regresses the multi-person 3D poses in a clean and efficient way, without relying on intermediate tasks.
SPEC: Seeing People in the Wild with an Estimated Camera
We then train a novel network that concatenates the camera calibration to the image features and uses these together to regress 3D body shape and pose.
