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Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction
[article]
2020
arXiv
pre-print
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We propose novel dynamic multiscale graph neural networks (DMGNN) to predict 3D skeleton-based human motions. ...
This multiscale graph is adaptive during training and dynamic across network layers. ...
Conclusion We build dynamic mutiscale graphs to represent a human body and propose dynamic multiscale graph neural networks (DMGNN) with an encoder-decoder framework for 3D skeleton-based human motion ...
arXiv:2003.08802v1
fatcat:pexi6av5d5fdrjbg7ywh5jsyjy
Learning Multiscale Correlations for Human Motion Prediction
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2103.10674v1 fatcat:r7o36qs5s5f27bx5rc2j2vc6h4
In this paper, we propose a novel multiscale graph convolution network (MGCN) to address this problem. ...
We evaluate our approach on two standard benchmark datasets for human motion prediction: Human3.6M and CMU motion capture dataset. ...
INTRODUCTION Human motion prediction aims to use the 3D skeleton data to predict a sequence of future human motions based on observed motion frames. ...
arXiv:2103.10674v2
fatcat:abb4fn4qcrce5ig3tubhfu36cu
Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction
[article]
2019
arXiv
pre-print
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3D skeleton-based action recognition and motion prediction are two essential problems of human activity understanding. ...
We conduct extensive experiments for skeleton-based action recognition and motion prediction with four datasets, NTU-RGB+D, Kinetics, Human3.6M, and CMU Mocap. ...
We propose novel symbiotic graph neural networks (Sym-GNN) to achieve 3D skeleton-based action recognition and motion prediction in a multitasking framework. ...
arXiv:1910.02212v1
fatcat:suouaghedffsfaszejnp375yxy
DMS-GCN: Dynamic Mutiscale Spatiotemporal Graph Convolutional Networks for Human Motion Prediction
[article]
2021
arXiv
pre-print
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In this paper, we propose a simple feed-forward deep neural network for motion prediction, which takes into account temporal smoothness and spatial dependencies between human body joints. ...
We design a Multi-scale Spatio-temporal graph convolutional networks (GCNs) to implicitly establish the Spatio-temporal dependence in the process of human movement, where different scales fused dynamically ...
Conclusion and Future Work In this paper, we build dynamic multiscale spatiotemporal graph convolutional networks to effectively predict future human poses from observed histories. ...
arXiv:2112.10365v1
fatcat:m6vaxivreba2xha6ffcp32ddqe
ANUBIS: Skeleton Action Recognition Dataset, Review, and Benchmark
[article]
2022
arXiv
pre-print
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We aim to provide a roadmap for new and existing researchers a on the landscapes of skeleton-based action recognition for new and existing researchers. ...
Compared with other data modalities, 3D human skeleton representations have extensive unique desirable characteristics, including succinctness, robustness, racial-impartiality, and many more. ...
and graph neural networks, in order to effectively capture the complex spatial-temporal movement patterns of human skeletons. ...
arXiv:2205.02071v3
fatcat:22iwew3p4faqxfp2ozbehyeffu
Motion Guided 3D Pose Estimation from Videos
[article]
2020
arXiv
pre-print
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We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose. ...
We design a new graph convolutional network architecture, U-shaped GCN (UGCN). ...
We structure 2D skeletons by a spatial-temporal graph and predict 3D locations via our U-shaped Graph Convolution Networks (UGCN). ...
arXiv:2004.13985v1
fatcat:7vnvq3pp4bdhjdfqn6tj4zhg54
Fusing Higher-Order Features in Graph Neural Networks for Skeleton-Based Action Recognition
[article]
2021
arXiv
pre-print
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Recent skeleton-based action recognition methods extract features from 3D joint coordinates as spatial-temporal cues, using these representations in a graph neural network for feature fusion to boost recognition ...
This simple fusion with popular spatial-temporal graph neural networks achieves new state-of-the-art accuracy in two large benchmarks, including NTU60 and NTU120, while employing fewer parameters and reduced ...
Graph neural networks started to attract attention [18] , [13] , [30] in skeleton recognition. In GCN-based models, a skeleton is treated as a graph, with joints as nodes and bones as edges. ...
arXiv:2105.01563v4
fatcat:oxzgap7efraqznhsqkkzl2rmwy
Action Recognition Using Action Sequences Optimization and Two-Stream 3D Dilated Neural Network
2022
Computational Intelligence and Neuroscience
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A two-stream 3D dilated neural network that integrates features of RGB and human skeleton information is also proposed. ...
The human skeleton information strengthens the deep representation of humans for robust processing, alleviating the interference of background changes, and the dilated CNN enlarges the receptive field ...
temporal information. (2) A two-stream 3D dilated convolution neural network integrates features of RGB and human skeleton information is also proposed. e human skeleton information strengthens the deep ...
doi:10.1155/2022/6608448
fatcat:m56ufg2z25goxdqzphmbsxaili
SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework for Person Re-Identification
[article]
2021
arXiv
pre-print
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In this paper, we for the first time propose a Self-supervised Multi-scale Skeleton Graph Encoding (SM-SGE) framework that comprehensively models human body, component relations, and skeleton dynamics ...
Specifically, we first devise multi-scale skeleton graphs with coarse-to-fine human body partitions, which enables us to model body structure and skeleton dynamics at multiple levels. ...
[16] propose PoseGait model, which learns 81 hand-crafted pose features of 3D skeleton data with deep convolutional neural networks (CNN) for gait-based human recognition. ...
arXiv:2107.01903v1
fatcat:5mduyl2xdjepxhyxlz43uo2ixe
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
[article]
2021
arXiv
pre-print
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In this paper, a simple yet effective multi-scale semantics-guided neural network (MS-SGN) is proposed for skeleton-based action recognition. ...
However, there is a trend of using very deep and complicated feedforward neural networks to model the skeleton sequence without considering the complexity in recent year. ...
Moreover, the scalable motion dynamics are in general under-explored in skeleton based action recognition. ...
arXiv:2111.03993v1
fatcat:v4pfwjjwhfbpvcijacqhpm3xxe
A Survey of Vision-Based Human Action Evaluation Methods
2019
Sensors
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This paper presents a comprehensive survey of approaches and techniques in action evaluation research, including motion detection and preprocessing using skeleton data, handcrafted feature representation ...
The research on human action evaluation differs by aiming to design computation models and evaluation approaches for automatically assessing the quality of human actions. ...
Acknowledgments: The authors would like to thank the anonymous reviewers for their valuable and insightful comments on an earlier version of this manuscript. ...
doi:10.3390/s19194129
pmid:31554229
pmcid:PMC6806217
fatcat:zgwsdv6xorfvxjck6rsjusezwe
Three-Dimensional Diffusion Model in Sports Dance Video Human Skeleton Detection and Extraction
2021
Advances in Mathematical Physics
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The research in this paper mainly includes as follows: for the principle of action recognition based on the 3D diffusion model convolutional neural network, the whole detection process is carried out from ...
graph matching, thus proposing a matching algorithm for discrete skeleton points and optimizing it for the skeleton dislocation and algorithm problems of human occlusion. ...
Principle of Action Recognition Based on THE 3D Diffusion Model Convolutional Neural Network. ...
doi:10.1155/2021/3772358
fatcat:chfv5pithngubfcaqsbfwj2msm
Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition
2022
Applied Sciences
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In this paper, we propose a multi-scale adaptive aggregate graph convolution network (MSAAGCN) for skeleton-based action recognition. ...
In recent years, there is a trend of using graph convolutional networks (GCNs) to model the human skeleton into a spatio-temporal graph to explore the internal connections of human joints that has achieved ...
In traditional deep learning methods, the skeleton sequence is usually fed into recurrent neural networks (RNN) or convolutional neural networks (CNN) for analysis, and its features are captured to predict ...
doi:10.3390/app12031402
fatcat:3ceyzh5tjbb3jbkvx4a5nbeytm
Kinematic Motion Retargeting via Neural Latent Optimization for Learning Sign Language
[article]
2022
arXiv
pre-print
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Traditional optimization-based methods are time-consuming and rely heavily on good initialization, while recent studies using feedforward neural networks suffer from poor generalization to unseen motions ...
Both the human skeleton and the robot structure are modeled as graphs to make better use of topological information. ...
to learn the significant information of human body skeletons for action recognition. [16] introduced a dynamic multiscale graph neural network to predict 3D skeleton-based human motions. [28] presented ...
arXiv:2103.08882v4
fatcat:khe7oz2qnzc27bv4hosyfacekm
MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction
[article]
2021
arXiv
pre-print
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Recently, graph convolutional network has been proven to be very effective to learn dynamic relations among pose joints, which is helpful for pose prediction. ...
In this paper, we propose a novel Multi-Scale Residual Graph Convolution Network (MSR-GCN) for human pose prediction task in the manner of end-to-end. ...
Science Foundation of China (62072191, 61802453, 61972160), in part by the Natural Science Foundation of Guangdong Province (2019A1515010860, 2021A1515012301), and in part by the Fundamental Research Funds for ...
arXiv:2108.07152v2
fatcat:x5ehkmkh6jcgtajbcupo6qpeya
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