scholarly journals Pointless Pose: Part Affinity Field-Based 3D Pose Estimation without Detecting Keypoints

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 929
Author(s):  
Jue Wang ◽  
Zhigang Luo
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
3D Pose Estimation ◽  
New Approach ◽  
Keypoint Detection ◽  
Body Regions ◽  
In The Wild ◽  
Orientation Vector ◽  
Human Pose ◽  
Local Image

Human pose estimation finds its application in an extremely wide domain and is therefore never pointless. We propose in this paper a new approach that, unlike any prior one that we are aware of, bypasses the 2D keypoint detection step based on which the 3D pose is estimated, and is thus pointless. Our motivation is rather straightforward: 2D keypoint detection is vulnerable to occlusions and out-of-image absences, in which case the 2D errors propagate to 3D recovery and deteriorate the results. To this end, we resort to explicitly estimating the human body regions of interest (ROI) and their 3D orientations. Even if a portion of the human body, like the lower arm, is partially absent, the predicted orientation vector pointing from the upper arm will take advantage of the local image evidence and recover the 3D pose. This is achieved, specifically, by deforming a skeleton-shaped puppet template to fit the estimated orientation vectors. Despite its simple nature, the proposed approach yields truly robust and state-of-the-art results on several benchmarks and in-the-wild data.

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 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 3D pose estimation of bats in the wild

2014 ◽  
Author(s):  
Mikhail Breslav ◽  
Nathan Fuller ◽  
Stan Sclaroff ◽  
Margrit Betke
Keyword(s):  
Pose Estimation ◽  
3D Pose Estimation ◽  
In The Wild

scholarly journals A Novel Multiple Person Pose Estimation Optimization Model Utilizing Genetic Algorithm

2021 ◽  
Vol 2129 (1) ◽  
pp. 012027
Author(s):  
Qing Zhang ◽  
Lei Ding ◽  
Kai Qing Zhou ◽  
Jian Feng Li
Keyword(s):  
Genetic Algorithm ◽  
Pose Estimation ◽  
Human Body ◽  
Optimization Model ◽  
Body Part ◽  
Body Parts ◽  
Position Information ◽  
Assembly Method ◽  
Optimal Value ◽  
Human Pose

Abstract For traditional human pose estimation models rely on a large amount of human body feature information, this paper proposes an optimization model using genetic algorithm to solve the problem of multiple person body part assembly. Different from other human body parts assembly method. The method proposed in this paper depends on the joints position information, namely the sum of the connection distances between the joints as the objective function, and finds the optimal value to obtain the best human pose assembly information. The simulation results show that compared with the traditional OpenPose model, the model proposed in this paper can obtain the same human skeleton using less position information.

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