scholarly journals Deep Learning Methods for 3D Human Pose Estimation under Different Supervision Paradigms: A Survey

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2267
Author(s):  
Dejun Zhang ◽  
Yiqi Wu ◽  
Mingyue Guo ◽  
Yilin Chen
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Literature Survey ◽  
Estimation Methods ◽  
Human Pose Estimation ◽  
Extensive Literature ◽  
Learning Methods ◽  
Human Pose ◽  
3D Human Pose Estimation ◽  
Research Studies

The rise of deep learning technology has broadly promoted the practical application of artificial intelligence in production and daily life. In computer vision, many human-centered applications, such as video surveillance, human-computer interaction, digital entertainment, etc., rely heavily on accurate and efficient human pose estimation techniques. Inspired by the remarkable achievements in learning-based 2D human pose estimation, numerous research studies are devoted to the topic of 3D human pose estimation via deep learning methods. Against this backdrop, this paper provides an extensive literature survey of recent literature about deep learning methods for 3D human pose estimation to display the development process of these research studies, track the latest research trends, and analyze the characteristics of devised types of methods. The literature is reviewed, along with the general pipeline of 3D human pose estimation, which consists of human body modeling, learning-based pose estimation, and regularization for refinement. Different from existing reviews of the same topic, this paper focus on deep learning-based methods. The learning-based pose estimation is discussed from two categories: single-person and multi-person. Each one is further categorized by data type to the image-based methods and the video-based methods. Moreover, due to the significance of data for learning-based methods, this paper surveys the 3D human pose estimation methods according to the taxonomy of supervision form. At last, this paper also enlists the current and widely used datasets and compares performances of reviewed methods. Based on this literature survey, it can be concluded that each branch of 3D human pose estimation starts with fully-supervised methods, and there is still much room for multi-person pose estimation based on other supervision methods from both image and video. Besides the significant development of 3D human pose estimation via deep learning, the inherent ambiguity and occlusion problems remain challenging issues that need to be better addressed.

scholarly journals 3D Single Person Pose Estimation Method Based on Deep Learning

2020 ◽  
Author(s):  
Xinrui Yuan ◽  
Hairong Wang ◽  
Jun Wang
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Estimation Method ◽  
Hybrid Network ◽  
Estimation Methods ◽  
Human Pose Estimation ◽  
Single Person ◽  
Depth Study ◽  
Human Pose ◽  
Set Up

In view of the significant effects of deep learning in graphics and image processing, research on human pose estimation methods using deep learning has attracted much attention, and many method models have been produced one after another. On the basis of tracking and in-depth study of domestic and foreign research results, this paper concentrates on 3D single person pose estimation methods, contrasts and analyzes three methods of end-to-end, staged and hybrid network models, and summarizes the characteristics of the methods. For evaluating method performance, set up an experimental environment, and utilize the Human3.6M data set to test several mainstream methods. The test results indicate that the hybrid network model method has a better performance in the field of human pose estimation.

scholarly journals 3D Human Pose Estimation Based on a Fully Connected Neural Network With Adversarial Learning Prior Knowledge

2021 ◽  
Vol 9 ◽  
Author(s):  
Lu Meng ◽  
Hengshang Gao
Keyword(s):  
Prior Knowledge ◽  
Pose Estimation ◽  
Optical Sensors ◽  
Estimation Methods ◽  
Generative Adversarial Networks ◽  
Human Pose Estimation ◽  
Natural Connection ◽  
Human Pose ◽  
Fully Connected ◽  
3D Human Pose Estimation

3D human pose estimation is more and more widely used in the real world, such as sports guidance, limb rehabilitation training, augmented reality, and intelligent security. Most existing human pose estimation methods are designed based on an RGB image obtained by one optical sensor, such as a digital camera. There is some prior knowledge, such as bone proportion and angle limitation of joint hinge motion. However, the existing methods do not consider the correlation between different joints from multi-view images, and most of them adopt fixed spatial prior constraints, resulting in poor generalizations. Therefore, it is essential to build a multi-view image acquisition system using optical sensors and customized algorithms for a 3D reconstruction of the human pose in the image. Inspired by generative adversarial networks (GAN), we used a data-driven method to learn the implicit spatial prior information and classified joints according to the natural connection characteristics. To accelerate the proposed method, we proposed a fully connected network with skip connections and used the SMPL model to make the 3D human body reconstruction. Experimental results showed that compared with other state-of-the-art methods, the joints’ average error of the proposed method was the smallest, which indicated the best performance. Moreover, the running time of the proposed method was 1.3 seconds per frame, which may not meet real-time requirements, but is still much faster than most existing methods.

scholarly journals Deep Reinforcement Learning for Active Human Pose Estimation

2020 ◽  
Vol 34 (07) ◽  
pp. 10835-10844
Author(s):  
Erik Gärtner ◽  
Aleksis Pirinen ◽  
Cristian Sminchisescu
Keyword(s):  
Reinforcement Learning ◽  
Pose Estimation ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Human Pose Estimation ◽  
Transition Functions ◽  
Processing Step ◽  
Complex Scenes ◽  
Human Pose ◽  
3D Human Pose Estimation

Most 3d human pose estimation methods assume that input – be it images of a scene collected from one or several viewpoints, or from a video – is given. Consequently, they focus on estimates leveraging prior knowledge and measurement by fusing information spatially and/or temporally, whenever available. In this paper we address the problem of an active observer with freedom to move and explore the scene spatially – in ‘time-freeze’ mode – and/or temporally, by selecting informative viewpoints that improve its estimation accuracy. Towards this end, we introduce Pose-DRL, a fully trainable deep reinforcement learning-based active pose estimation architecture which learns to select appropriate views, in space and time, to feed an underlying monocular pose estimator. We evaluate our model using single- and multi-target estimators with strong result in both settings. Our system further learns automatic stopping conditions in time and transition functions to the next temporal processing step in videos. In extensive experiments with the Panoptic multi-view setup, and for complex scenes containing multiple people, we show that our model learns to select viewpoints that yield significantly more accurate pose estimates compared to strong multi-view baselines.

scholarly journals Multi-View Pose Generator Based on Deep Learning for Monocular 3D Human Pose Estimation

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1116 ◽  
Author(s):  
Jun Sun ◽  
Mantao Wang ◽  
Xin Zhao ◽  
Dejun Zhang
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Data Augmentation ◽  
Estimation Method ◽  
Human Pose Estimation ◽  
Convolutional Network ◽  
3D Pose Estimation ◽  
Single View ◽  
Human Pose ◽  
3D Human Pose Estimation

In this paper, we study the problem of monocular 3D human pose estimation based on deep learning. Due to single view limitations, the monocular human pose estimation cannot avoid the inherent occlusion problem. The common methods use the multi-view based 3D pose estimation method to solve this problem. However, single-view images cannot be used directly in multi-view methods, which greatly limits practical applications. To address the above-mentioned issues, we propose a novel end-to-end 3D pose estimation network for monocular 3D human pose estimation. First, we propose a multi-view pose generator to predict multi-view 2D poses from the 2D poses in a single view. Secondly, we propose a simple but effective data augmentation method for generating multi-view 2D pose annotations, on account of the existing datasets (e.g., Human3.6M, etc.) not containing a large number of 2D pose annotations in different views. Thirdly, we employ graph convolutional network to infer a 3D pose from multi-view 2D poses. From experiments conducted on public datasets, the results have verified the effectiveness of our method. Furthermore, the ablation studies show that our method improved the performance of existing 3D pose estimation networks.

scholarly journals Human pose, hand and mesh estimation using deep learning: a survey

2022 ◽  
Author(s):  
Mukhiddin Toshpulatov ◽  
Wookey Lee ◽  
Suan Lee ◽  
Arousha Haghighian Roudsari
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Pose Estimation ◽  
State Of The Art ◽  
Significant Problem ◽  
Estimation Methods ◽  
Human Pose Estimation ◽  
Estimation Techniques ◽  
The Past ◽  
Human Pose

AbstractHuman pose estimation is one of the issues that have gained many benefits from using state-of-the-art deep learning-based models. Human pose, hand and mesh estimation is a significant problem that has attracted the attention of the computer vision community for the past few decades. A wide variety of solutions have been proposed to tackle the problem. Deep Learning-based approaches have been extensively studied in recent years and used to address several computer vision problems. However, it is sometimes hard to compare these methods due to their intrinsic difference. This paper extensively summarizes the current deep learning-based 2D and 3D human pose, hand and mesh estimation methods with a single or multi-person, single or double-stage methodology-based taxonomy. The authors aim to make every step in the deep learning-based human pose, hand and mesh estimation techniques interpretable by providing readers with a readily understandable explanation. The presented taxonomy has clearly illustrated current research on deep learning-based 2D and 3D human pose, hand and mesh estimation. Moreover, it also provided dataset and evaluation metrics for both 2D and 3DHPE approaches.

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