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3D Human Pose Estimation: A Survey

Frontiers in Computing and Intelligent Systems, 2023
This comprehensive review article explores the latest research advancements in the realm of estimating 3D human pose. Traditional methods such as PSM, SVM are discussed. Besides, this review also talks about deep learning-based approaches, including direct approaches, 2D-to-3D lifting and volumetric model approach for single person, top-down approaches
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2D–3D pose consistency-based conditional random fields for 3D human pose estimation [PDF]

Computer Vision and Image Understanding, 2018
This study considers the 3D human pose estimation problem in a single RGB image by proposing a conditional random field (CRF) model over 2D poses, in which the 3D pose is obtained as a byproduct of the inference process. The unary term of the proposed CRF model is defined based on a powerful heat-map regression network, which has been proposed for 2D ...
Ju Yong Chang, Kyoung Mu Lee
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HTNet: Human Topology aware network for 3d Human pose estimation

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023
3D human pose estimation errors would propagate along the human body topology and accumulate at the end joints of limbs. Inspired by the backtracking mechanism in automatic control systems, we design an Intra-Part Constraint module that utilizes the parent nodes as the reference to build topological constraints for end joints at the part level. Further
Cai, Jialun, Liu, Hong, Ding, Runwei, Li, Wenhao, Wu, Jianbing, Ban, Miaoju   +5 more
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Motion Capture for Sporting Events Based on Graph Convolutional Neural Networks and Single Target Pose Estimation Algorithms

Applied Sciences, 2023
Human pose estimation refers to accurately estimating the position of the human body from a single RGB image and detecting the location of the body. It serves as the basis for several computer vision tasks, such as human tracking, 3D reconstruction, and ...
Chengpeng Duan, Bingliang Hu, Wei Liu, Jie Song   +3 more
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VIBE: Video Inference for Human Body Pose and Shape Estimation

, 2020
Human motion is fundamental to understanding behavior. Despite progress on single-image 3D pose and shape estimation, existing video-based state-of-the-art methods fail to produce accurate and natural motion sequences due to a lack of ground-truth 3D ...
Athanasiou, Nikos, Black, Michael J., Kocabas, Muhammed   +2 more
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A Survey on Depth Ambiguity of 3D Human Pose Estimation

Applied Sciences, 2022
Depth ambiguity is one of the main challenges of three-dimensional (3D) human pose estimation (HPE). The recent strategies of disambiguating have brought significant progress and remarkable breakthroughs in the field of 3D human pose estimation (3D HPE).
Siqi Zhang, Chaofang Wang, Wenlong Dong, Bin Fan   +3 more
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V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map

, 2018
Most of the existing deep learning-based methods for 3D hand and human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of keypoints, such as hand or human body ...
Chang, Ju Yong, Lee, Kyoung Mu, Moon, Gyeongsik   +2 more
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Monocular 3D Human Pose Estimation by Classification [PDF]

2011 IEEE International Conference on Multimedia and Expo, 2011
We present a novel approach to 2D and 3D human pose estimation in monocular images by building on and improving recent advances in this field. We take the full body pose as a combination of a 3D pose and a viewpoint and in this way define classes that are then learned by a classifier. Compared to part based approaches, our approach does not suffer from
Greif, Thomas, Sengupta, Debabrata, Lienhart, Rainer (Prof. Dr.)   +2 more
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Hybrid LSTM & Transformer for 3D Human Pose Estimation [PDF]

ITM Web of Conferences
3D human pose estimation (3DHPE) has evolved into a sophisticated and pivotal technique, emerging as a prominent research focus in computer vision and robotics, based on the power of deep neural networks.
Su Longjie
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3D Human Pose Estimation Using Möbius Graph Convolutional Networks

, 2022
3D human pose estimation is fundamental to understanding human behavior. Recently, promising results have been achieved by graph convolutional networks (GCNs), which achieve state-of-the-art performance and provide rather light-weight architectures. However, a major limitation of GCNs is their inability to encode all the transformations between joints ...
Azizi N., Possegger H., Rodola E., Bischof H.   +3 more
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