3D Multi-Person Pose Estimation (absolute)
12 papers with code • 1 benchmarks • 2 datasets
This task aims to solve absolute 3D multi-person pose Estimation (camera-centric coordinates). No ground truth human bounding box and human root joint coordinates are used during testing stage.
( Image credit: RootNet )
MuPoTS-3D
Relative Human
Most implemented papers
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
xingyizhou/pose-hg-3d
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ICCV 2017
We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure.
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
Daniil-Osokin/lightweight-human-pose-estimation-3d-demo.pytorch
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Our approach uses novel occlusion-robust pose-maps (ORPM) which enable full body pose inference even under strong partial occlusions by other people and objects in the scene.
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
mks0601/3DMPPE_POSENET_RELEASE
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ICCV 2019
Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case.
RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density Estimates
First, problem complexity is reduced to the use of a single parameter (choice of k nearest neighbors), and second, an improved ability for handling large variations in cluster density (heterogeneous density).
Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision
vegesm/wdspose
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In 3D human pose estimation one of the biggest problems is the lack of large, diverse datasets.
HDNet: Human Depth Estimation for Multi-Person Camera-Space Localization
jiahaoLjh/HumanDepth
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ECCV 2020
Current works on multi-person 3D pose estimation mainly focus on the estimation of the 3D joint locations relative to the root joint and ignore the absolute locations of each pose.
SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation
zju3dv/smap
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ECCV 2020
Recovering multi-person 3D poses with absolute scales from a single RGB image is a challenging problem due to the inherent depth and scale ambiguity from a single view.
Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos
3dpose/GnTCN
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To tackle this problem, we propose a novel framework integrating graph convolutional networks (GCNs) and temporal convolutional networks (TCNs) to robustly estimate camera-centric multi-person 3D poses that do not require camera parameters.
Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks
3dpose/3D-Multi-Person-Pose
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CVPR 2021
Besides the integration of top-down and bottom-up networks, unlike existing pose discriminators that are designed solely for single person, and consequently cannot assess natural inter-person interactions, we propose a two-person pose discriminator that enforces natural two-person interactions.
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.
