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Table of Contents

3D Whole-Body Mesh Recovery

2022

• PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images - [code] [paper] - Arxiv, PyMAF-X

Hongwen Zhang, Yating Tian, Yuxiang Zhang, Mengcheng Li, Liang An, Zhenan Sun, Yebin Liu

• Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation - [code] [paper] - CVPRW, Hand4Whole

Gyeongsik Moon, Hongsuk Choi, Kyoung Mu Lee

2021

• Collaborative Regression of Expressive Bodies using Moderation - [code] [paper] - 3DV, PIXIE

Yao Feng, Vasileios Choutas, Timo Bolkart, Dimitrios Tzionas, Michael J. Black

• FrankMocap: Fast Monocular 3D Hand and Body Motion Capture by Regression and Integration - [code] [paper] - ICCV workshop, FrankMocap

Ye Yuan, Umar Iqbal, Pavlo Molchanov, Kris Kitani, Jan Kautz

• Monocular Real-time Full Body Capture with Inter-part Correlations - [paper] - CVPR 21, Zhou et al

Yuxiao Zhou, Marc Habermann, Ikhsanul Habibie, Ayush Tewari, Christian Theobalt, Feng Xu

2020

• Whole-Body Human Pose Estimation in the Wild - [code] [paper] - ECCV, COCO-WholeBody, (only Keypoint)

Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping

• Monocular Expressive Body Regression through Body-Driven Attention - [code] [paper] - ECCV, Expose

Vasileios Choutas, Georgios Pavlakos, Timo Bolkart, Dimitrios Tzionas, Michael J. Black

• DOPE: Distillation Of Part Experts for whole-body 3D pose estimation in the wild - [code] [paper] - ECCV, DOPE, (only Keypoint)

Philippe Weinzaepfel, Romain Brégier, Hadrien Combaluzier, Vincent Leroy, Grégory Rogez

2019

• Expressive Body Capture: 3D Hands, Face, and Body from a Single Image -[code] [paper] - CVPR 19, SMPL-X

Pavlakos, Georgios and Choutas, Vasileios and Ghorbani, Nima and Bolkart, Timo and Osman, Ahmed A. A. and Tzionas, Dimitrios and Black, Michael J.


Single-Person 2D Pose Estimation

2022

• SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation - [code] [paper] - ECCV 22, SimCC

3D Mesh Recovery from video

2022

• GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras - [code] [paper] - CVPR 22, GLAMR

Ye Yuan, Umar Iqbal, Pavlo Molchanov, Kris Kitani, Jan Kautz

• Capturing Humans in Motion: Temporal-Attentive 3D Human Pose and Shape Estimation from Monocular Video - [paper] - CVPR 22, MPS-Net

Wen-Li Wei, Jen-Chun Lin, Tyng-Luh Liu, Hong-Yuan Mark Liao

2021

• Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video - [code] [paper] - CVPR 21, TCMR

Hongsuk Choi, Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee

2020

• VIBE: Video Inference for Human Body Pose and Shape Estimation - [code] [paper] - CVPR 20, VIBE

Muhammed Kocabas, Nikos Athanasiou, Michael J. Black


3D People Tracking

2022

• Tracking People by Predicting 3D Appearance, Location & Pose - [code] [paper] - CVPR 22, PHALP

Jathushan Rajasegaran, Georgios Pavlakos, Angjoo Kanazawa, Jitendra Malik

2021

• TesseTrack: End-to-End Learnable Multi-Person Articulated 3D Pose Tracking - [paper] - CVPR 21, TesseTrack

N Dinesh Reddy, Laurent Guigues, Leonid Pishchulin, Jayan Eledath, Srinivasa G. Narasimhan

• Tracking People with 3D Representations - [code] [paper] - NeurIPS 21, HMAR

Jathushan Rajasegaran, Georgios Pavlakos, Angjoo Kanazawa, Jitendra Malik


Multi-Person 3D Mesh Recovery

2022

• Putting People in their Place: Monocular Regression of 3D People in Depth - [code] [paper] [preview] - CVPR 22, BEV

Yu Sun, Wu Liu, Qian Bao, Yili Fu, Tao Mei, Michael J. Black

• Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes - [code] [paper] - CVPR 22, 3DCrowdNet

Hongsuk Choi, Gyeongsik Moon, JoonKyu Park, Kyoung Mu Lee

2021

• Monocular, One-stage, Regression of Multiple 3D People - [code] [paper] - ICCV 21, ROMP

Yu Sun, Qian Bao, Wu Liu, Yili Fu, Michael J. Black, Tao Mei


Single-Person 3D Mesh Recovery

2021

• HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation - [code] [paper] - CVPR 21, HybrIK

Jiefeng Li, Chao Xu, Zhicun Chen, Siyuan Bian, Lixin Yang, Cewu Lu

• Mesh Graphormer- [code] [paper] - ICCV 21, Mesh Graphormer

Kevin Lin, Lijuan Wang, Zicheng Liu

• End-to-End Human Pose and Mesh Reconstruction with Transformers - [code] [paper] - CVPR 21, METRO

Kevin Lin, Lijuan Wang, Zicheng Liu

Multi-Person 2D Pose Estimation

2022

• Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation - [paper] - CVPR 22, DAS

Zitian Wang, Xuecheng Nie, Xiaochao Qu, Yunpeng Chen, Si Liu

• Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation - [code] [paper] - CVPR 22, LOGO-CAP

Nan Xue, Tianfu Wu, Gui-Song Xia, Liangpei Zhang

• End-to-End Multi-Person Pose Estimation with Transformers - [code] [paper] - CVPR 22, PETR

Dahu Shi1, Xing Wei2, Liangqi Li, Ye Ren, Wenming Tan

• Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation - [code] [paper] - CVPR 22, Lite Pose

Yihan Wang, Muyang Li, Han Cai, Wei-Ming Chen, Song Han

• Contextual Instance Decoupling for Robust Multi-Person Pose Estimation - [code] [paper] - CVPR 22, CID

Dongkai Wang, Shiliang Zhang

• Location-Free Human Pose Estimation - [paper] - CVPR 22

Xixia Xu, Yingguo Gao, Ke Yan, Xue Lin, Qi Zou

2021

• Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation - [code] [paper] - Arxiv 21.11, KAPAO

William McNally, Kanav Vats, Alexander Wong, John McPhee

• Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression - [code] [paper] - CVPR 21, DEKR

Zigang Geng, Ke Sun, Bin Xiao, Zhaoxiang Zhang, Jingdong Wang

• OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association - [code] [paper] - Arxiv 21.03, OpenPifPaf

Sven Kreiss, Lorenzo Bertoni, Alexandre Alahi

• Human Pose Regression with Residual Log-likelihood Estimation - [code] [paper] - ICCV 21, RLE

Jiefeng Li, Siyuan Bian, Ailing Zeng, Can Wang, Bo Pang, Wentao Liu, Cewu Lu

• Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation - [code] [paper] - ICCV 21, MIPNet

Rawal Khirodkar, Visesh Chari, Amit Agrawal, Ambrish Tyagi

• Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference - [code] [paper] - NeurIPS 21, PINet

Dongkai Wang, Shiliang Zhang, Gang Hua

2020

• HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation - [code] [paper] - CVPR 20, HigherHRNet

Bowen Cheng, Bin Xiao, Jingdong Wang, Honghui Shi, Thomas S. Huang, Lei Zhang

• Distribution-Aware Coordinate Representation for Human Pose Estimation - [code] [paper] - CVPR 20, DARK

Feng Zhang, Xiatian Zhu, Hanbin Dai, Mao Ye, Ce Zhu

2019

• PifPaf: Composite Fields for Human Pose Estimation - [code] [paper] - CVPR 19, PifPaf

Sven Kreiss, Lorenzo Bertoni, Alexandre Alahi


Multi-Person 3D Pose Estimation

2022

• Single-Stage is Enough: Multi-Person Absolute 3D Pose Estimation - [paper] - CVPR 22, DRM

Lei Jin, Chenyang Xu, Xiaojuan Wang, Yabo Xiao, Yandong Guo, Xuecheng Nie, Jian Zhao


Backbone

2022

• ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation - [code] [paper] - Arxiv 22, ViTPose

Yufei Xu, Jing Zhang, Qiming Zhang, Dacheng Tao

2021

• HRFormer: High-Resolution Transformer for Dense Prediction - [code] [paper] - NeurIPS 21, HRFormer

Yuhui Yuan, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, Jingdong Wang


Datasets

borrowed from 'Recovering 3D Human Mesh from Monocular Images: A Survey'

Rendered Datasets

Dataset # Frames # Scenes # Subjects # Subjects Per Frame In-the-wild Mesh Type Mesh Annotation Source
SURREAL 6.5M 2,607 145 1 - SMPL
GTA-Human 1.4M - >600 - - SMPL
AGORA 17K - >350 5~15 - SMPL-X
THUman2.0 - - ~200 1 - SMPL-X
MultiHuman - - ~50 1~3 - SMPL-X

Marker/Sensor based MoCap

Dataset # Frames # Scenes # Subjects # Subjects Per Frame In-the-wild Mesh Type Mesh Annotation Source
HumanEva 80K 1 4 1 - -
Human3.6M 3.6M 1 11 1 - SMPL-X NeuralAnnot
3DPW >51K 60 7 1~2 yes SMPL-X NeuralAnnot

Marker-less Multi-view MoCap

Dataset # Frames # Scenes # Subjects # Subjects Per Frame In-the-wild Mesh Type Mesh Annotation Source
CMU Panoptic 1.5M 1 40 3~8 - -
MPI-INF-3DHP >1.3M 1 8 1 yes SMPL-X NeuralAnnot
MuCo-3DHP 200K 1 8 1~4 - -
MuPoTs-3D >8K 20 8 3 yes -
MannequinChallenge 24,428 567 742 5 yes SMPL
3DOH50K 51,600 1 - 1 - SMPL
Mirrored-Human 1.8M >200 >200 >=1 yes SMPL
MTC 834K 1 40 1 - -
EHF 100 1 1 1 - SMPL-X
HUMBI 17.3M 1 772 1 - SMPL
ZJU-MoCap - 1 9 1 - SMPL-X

Datasets with pseudo 3D GT

Dataset # Frames # Scenes # Subjects # Subjects Per Frame In-the-wild Mesh Type Mesh Annotation Source
LSP 2K - - 1 yes SMPL
LSP-Extended 10K - - 1 yes SMPL
PennAction 77K 2,326 2,326 1 yes SMPL
MSCOCO 38K - - >=1 yes SMPL
MPII 24,920 3,913 >40K >=1 yes SMPL
UP-3D 8,515 - - 1 yes SMPL
PoseTrack 66,374 550 550 >1 yes SMPL-X NeuralAnnot
SSP-3D 311 62 62 1 yes SMPL
OCHuman 4,731 - 8110 >1 yes SMPL
MTP 3,731 - 148 1 yes SMPL-X

Experiments

COCO test-dev

  • borrowed from KAPAO

ex_screenshot

Releases

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Packages

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