Modulated Graph Convolutional Network for 3D Human Pose Estimation

Zou, Zhiming; Tang, Wei

doi:10.1109/iccv48922.2021.01128

Cited by 95 publications

(74 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It forecasts the equivalent poses in the frame for the tracklet and takes as input the past pose tracklet. Zou et al [15] proposed a Modulated Graph Convolutional Network (GCN) for 3D HPE. It comprises two major elements: affinity and weight modulations.…”

Section: Literature Reviewmentioning

confidence: 99%

Squirrel Search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

Ishwarya¹,

Nithya²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Human pose estimation (HPE) is a procedure for determining the structure of the body pose and it is considered a challenging issue in the computer vision (CV) communities. HPE finds its applications in several fields namely activity recognition and human-computer interface. Despite the benefits of HPE, it is still a challenging process due to the variations in visual appearances, lighting, occlusions, dimensionality, etc. To resolve these issues, this paper presents a squirrel search optimization with a deep convolutional neural network for HPE (SSDCNN-HPE) technique. The major intention of the SSDCNN-HPE technique is to identify the human pose accurately and efficiently. Primarily, the video frame conversion process is performed and pre-processing takes place via bilateral filtering-based noise removal process. Then, the EfficientNet model is applied to identify the body points of a person with no problem constraints. Besides, the hyperparameter tuning of the EfficientNet model takes place by the use of the squirrel search algorithm (SSA). In the final stage, the multiclass support vector machine (M-SVM) technique was utilized for the identification and classification of human poses. The design of bilateral filtering followed by SSA based EfficientNet model for HPE depicts the novelty of the work. To demonstrate the enhanced outcomes of the SSDCNN-HPE approach, a series of simulations are executed. The experimental results reported the betterment of the SSDCNN-HPE system over the recent existing techniques in terms of different measures.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Squirrel Search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

Ishwarya¹,

Nithya²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Graph Convolution Networks (GCN) have received considerable attention in recent years due to their ability to intuitively model data, such as articulated models. In particular, many studies have adopted GCN to model 3D human pose estimation [11], [12]. In the context of human skeletons, joints and bones are mapped to vertices and edges, respectively, thus forming a graph represented mathematically as G = v, .…”

Section: A Preliminarymentioning

confidence: 99%

A Modular Multi-stage Lightweight Graph Transformer Network for Human Pose and Shape Estimation from 2D Human Pose

Ali¹,

Pinyoanuntapong²,

Wang³

et al. 2023

Preprint

View full text Add to dashboard Cite

In this research, we address the challenge faced by existing deep learning-based human mesh reconstruction methods in balancing accuracy and computational efficiency. These methods typically prioritize accuracy, resulting in large network sizes and excessive computational complexity, which may hinder their practical application in real-world scenarios, such as virtual reality systems. To address this issue, we introduce a modular multi-stage lightweight graph-based transformer network for human pose and shape estimation from 2D human pose, a pose-based human mesh reconstruction approach that prioritizes computational efficiency without sacrificing reconstruction accuracy. Our method consists of a 2D-to-3D lifter module that utilizes graph transformers to analyze structured and implicit joint correlations in 2D human poses, and a mesh regression module that combines the extracted pose features with a mesh template to produce the final human mesh parameters.

show abstract

“…[4] proposes to use recurrent networks to mine temporal relationships between inputs through long and short-term memory units. [31] models the human pose joints in the form of a graph structure.…”

Section: Related Workmentioning

confidence: 99%

(Fusionformer):Exploiting the Joint Motion Synergy with Fusion Network Based On Transformer for 3D Human Pose Estimation

Yu¹

2022

Preprint

View full text Add to dashboard Cite

For the current 3D human pose estimation task, a group of methods mainly learn the rules of 2D-3D projection from spatial and temporal correlation. However, earlier methods model the global features of the entire body joint in the time domain, but ignore the motion trajectory of individual joint. The recent work [29] considers that there are differences in motion between different joints and deals with the temporal relationship of each joint separately. However, we found that different joints show the same movement trends under some specific actions. Therefore, our proposed Fusionformer method introduces a self-trajectory module and a mutual-trajectory module based on the spatio-temporal module .After that, the global spatio-temporal features and local joint trajectory features are fused through a linear network in a parallel manner. To eliminate the influence of bad 2D poses on 3D projections, finally we also introduce a pose refinement network to balance the consistency of 3D projections. In addition, we evaluate the proposed method on two benchmark datasets (Hu-man3.6M, MPI-INF-3DHP). Comparing our method with the baseline method poseformer,the results show an improvement of 2.4% MPJPE and 4.3% P-MPJPE on the Human3.6M dataset, respectively.

show abstract

Modulated Graph Convolutional Network for 3D Human Pose Estimation

Cited by 95 publications

References 43 publications

Squirrel Search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

Squirrel Search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

A Modular Multi-stage Lightweight Graph Transformer Network for Human Pose and Shape Estimation from 2D Human Pose

(Fusionformer):Exploiting the Joint Motion Synergy with Fusion Network Based On Transformer for 3D Human Pose Estimation

Contact Info

Product

Resources

About