Towards Part-Aware Monocular 3D Human Pose Estimation: An Architecture Search Approach

Chen, Zerui; Huang, Yan; Yu, Hongyuan; Xue, Bin; Han, Kuang-Wei; Guo, Yuanhao; Wang, Liang

doi:10.1007/978-3-030-58580-8_42

Cited by 24 publications

(9 citation statements)

References 53 publications

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“…Table 4 shows the results of the first test made with Protocol 1. The accuracy of Chen et al [51] is the best (32.7mm). Being nearly the same as the accuracy of Ours-L (32.8mm), it is obtained after spending more than 3 times GFLOPS, i.e., 14.1 vs. 4.30.…”

Section: B Evaluation Resultsmentioning

confidence: 85%

ConvNeXtPose: A Fast Accurate Method for 3D Human Pose Estimation and Its AR Fitness Application in Mobile Devices

Nguyen,

Kim,

et al. 2023

IEEE Access

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In general, 3D human-pose estimation requires high-performance computing resources. Existing methods working on mobile devices trade off accuracy in return for increased efficiency, often making the estimation accuracy far from sufficient for developing serious applications. In this paper, we present a mobile 3D humanpose estimation model, achieving real-time performances with a well-designed balance between efficiency and accuracy. As the backbone, our model leverages the cutting-edge ConvNeXt architecture, renowned for its feature extraction capabilities. We enhance its performance through strategic architectural modifications and incorporation of depthwise separable convolutions in the upsampling module. The experiments made with the Human3.6M dataset show that the accuracy delivered by our model is comparable to that of the stateof-the-art models, consuming significantly fewer computational resources. To showcase the practicality of our model, we present a prototype of an AR fitness application. Built upon our 3D human pose estimation model, it helps trainees recreate trainers' poses from reference images. The effectiveness of the application is validated via experiments and evaluations. The source code can be found at: https://github.com/medialab-ku/ ConvNeXtPose.

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Section: B Evaluation Resultsmentioning

confidence: 85%

ConvNeXtPose: A Fast Accurate Method for 3D Human Pose Estimation and Its AR Fitness Application in Mobile Devices

Nguyen,

Kim,

et al. 2023

IEEE Access

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“…Neural Architecture Search (NAS) [296] can search the optimal architecture for estimating each body part [297], [298]. Also, NAS can be used for discovering efficient HPE network architectures to reduce the computational cost [299].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Human Pose Estimation: A Survey

Zheng¹,

Wu²,

Yang³

et al. 2020

Preprint

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Human pose estimation aims to locate the human body parts and build human body representation (e.g., body skeleton) from input data such as images and videos. It has drawn increasing attention during the past decade and has been utilized in a wide range of applications including human-computer interaction, motion analysis, augmented reality, and virtual reality. Although the recently developed deep learning-based solutions have achieved high performance in human pose estimation, there still remain challenges due to insufficient training data, depth ambiguities, and occlusion. The goal of this survey paper is to provide a comprehensive review of recent deep learning-based solutions for both 2D and 3D pose estimation via a systematic analysis and comparison of these solutions based on their input data and inference procedures. More than 240 research papers since 2014 are covered in this survey. Furthermore, 2D and 3D human pose estimation datasets and evaluation metrics are included. Quantitative performance comparisons of the reviewed methods on popular datasets are summarized and discussed. Finally, the challenges involved, applications, and future research directions are concluded. We also provide a regularly updated project page: https://github.com/zczcwh/DL-HPE

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“…Existing studies on 3D pose estimation with image-based input are mainly conducted from two aspects. One is to solve the problem of human occlusion, and the other is to improve the generalization ability of the model [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. Most existing methods are based on regression.…”

Section: Image-based Inputmentioning

confidence: 99%

Overview of 3D Human Pose Estimation

Lin¹,

Li²,

Qin³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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3D human pose estimation is a major focus area in the field of computer vision, which plays an important role in practical applications. This article summarizes the framework and research progress related to the estimation of monocular RGB images and videos. An overall perspective of methods integrated with deep learning is introduced. Novel image-based and video-based inputs are proposed as the analysis framework. From this viewpoint, common problems are discussed. The diversity of human postures usually leads to problems such as occlusion and ambiguity, and the lack of training datasets often results in poor generalization ability of the model. Regression methods are crucial for solving such problems. Considering image-based input, the multi-view method is commonly used to solve occlusion problems. Here, the multi-view method is analyzed comprehensively. By referring to video-based input, the human prior knowledge of restricted motion is used to predict human postures. In addition, structural constraints are widely used as prior knowledge. Furthermore, weakly supervised learning methods are studied and discussed for these two types of inputs to improve the model generalization ability. The problem of insufficient training datasets must also be considered, especially because 3D datasets are usually biased and limited. Finally, emerging and popular datasets and evaluation indicators are discussed. The characteristics of the datasets and the relationships of the indicators are explained and highlighted. Thus, this article can be useful and instructive for researchers who are lacking in experience and find this field confusing. In addition, by providing an overview of 3D human pose estimation, this article sorts and refines recent studies on 3D human pose estimation. It describes kernel problems and common useful methods, and discusses the scope for further research.

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Towards Part-Aware Monocular 3D Human Pose Estimation: An Architecture Search Approach

Cited by 24 publications

References 53 publications

ConvNeXtPose: A Fast Accurate Method for 3D Human Pose Estimation and Its AR Fitness Application in Mobile Devices

ConvNeXtPose: A Fast Accurate Method for 3D Human Pose Estimation and Its AR Fitness Application in Mobile Devices

Deep Learning-Based Human Pose Estimation: A Survey

Overview of 3D Human Pose Estimation

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