scholarly journals In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations

2019 ◽  
Author(s):  
Ikhsanul Habibie ◽  
Weipeng Xu ◽  
Dushyant Mehta ◽  
Gerard Pons-Moll ◽  
Christian Theobalt
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
In The Wild ◽  
Human Pose ◽  
3D Representations

scholarly journals Monocular 3D Human Pose Estimation in the Wild Using Improved CNN Supervision

2017 ◽  
Author(s):  
Dushyant Mehta ◽  
Helge Rhodin ◽  
Dan Casas ◽  
Pascal Fua ◽  
Oleksandr Sotnychenko ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
In The Wild ◽  
Human Pose ◽  
3D Human Pose Estimation

scholarly journals Whole-Body Human Pose Estimation in the Wild

2020 ◽  
pp. 196-214 ◽  
Author(s):  
Sheng Jin ◽  
Lumin Xu ◽  
Jin Xu ◽  
Can Wang ◽  
Wentao Liu ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Whole Body ◽  
Human Pose Estimation ◽  
In The Wild ◽  
Human Pose

scholarly journals A Baseline for Cross-Database 3D Human Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3769
Author(s):  
Michał Rapczyński ◽  
Philipp Werner ◽  
Sebastian Handrich ◽  
Ayoub Al-Hamadi
Keyword(s):  
Pose Estimation ◽  
Real Life ◽  
Human Pose Estimation ◽  
3D Pose Estimation ◽  
Multiple Datasets ◽  
In The Wild ◽  
Scale Normalization ◽  
Human Pose ◽  
Future Work ◽  
3D Human Pose Estimation

Vision-based 3D human pose estimation approaches are typically evaluated on datasets that are limited in diversity regarding many factors, e.g., subjects, poses, cameras, and lighting. However, for real-life applications, it would be desirable to create systems that work under arbitrary conditions (“in-the-wild”). To advance towards this goal, we investigated the commonly used datasets HumanEva-I, Human3.6M, and Panoptic Studio, discussed their biases (that is, their limitations in diversity), and illustrated them in cross-database experiments (for which we used a surrogate for roughly estimating in-the-wild performance). For this purpose, we first harmonized the differing skeleton joint definitions of the datasets, reducing the biases and systematic test errors in cross-database experiments. We further proposed a scale normalization method that significantly improved generalization across camera viewpoints, subjects, and datasets. In additional experiments, we investigated the effect of using more or less cameras, training with multiple datasets, applying a proposed anatomy-based pose validation step, and using OpenPose as the basis for the 3D pose estimation. The experimental results showed the usefulness of the joint harmonization, of the scale normalization, and of augmenting virtual cameras to significantly improve cross-database and in-database generalization. At the same time, the experiments showed that there were dataset biases that could not be compensated and call for new datasets covering more diversity. We discussed our results and promising directions for future work.

scholarly journals SMPLy Benchmarking 3D Human Pose Estimation in the Wild

2020 ◽  
Author(s):  
Vincent Leroy ◽  
Philippe Weinzaepfel ◽  
Romain Bregier ◽  
Hadrien Combaluzier ◽  
Gregory Rogez
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
In The Wild ◽  
Human Pose ◽  
3D Human Pose Estimation
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