首页|FMR-GNet:Forward Mix-Hop Spatial-Temporal Residual Graph Network for 3D Pose Estimation

FMR-GNet:Forward Mix-Hop Spatial-Temporal Residual Graph Network for 3D Pose Estimation

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Graph convolutional networks that leverage spatial-temporal information from skeletal data have emerged as a popular approach for 3D human pose estimation.However,comprehensively modeling consistent spatial-temporal dependencies among the body joints remains a challenging task.Current approaches are limited by perform-ing graph convolutions solely on immediate neighbors,deploying separate spatial or temporal modules,and utilizing single-pass feedforward architectures.To solve these limitations,we propose a forward multi-scale residual graph con-volutional network(FMR-GNet)for 3D pose estimation from monocular video.First,we introduce a mix-hop spatial-temporal attention graph convolution layer that effectively aggregates neighboring features with learnable weights over large receptive fields.The attention mechanism enables dynamically computing edge weights at each layer.Second,we devise a cross-domain spatial-temporal residual module to fuse multi-scale spatial-temporal convolutional features through residual connections,explicitly modeling interdependencies across spatial and temporal domains.Third,we integrate a forward dense connection block to propagate spatial-temporal representations across network layers,en-abling high-level semantic skeleton information to enrich lower-level features.Comprehensive experiments conducted on two challenging 3D human pose estimation benchmarks,namely Human3.6M and MPI-INF-3DHP,demonstrate that the proposed FMR-GNet achieves superior performance,surpassing the most state-of-the-art methods.

3D human pose estimationSpatial-temporal graph convolution networkCross-domain residual connection

Honghong YANG、Hongxi LIU、Yumei ZHANG、Xiaojun WU

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Key Laboratory of Modern Teaching Technology,Ministry of Education,Shaanxi Normal University,Xi'an 710062,China

Key Laboratory of Intelligent Computing and Service Technology for Folk Song,Ministry of Culture and Tourism,Xi'an 710062,China

School of Computer Science,Shaanxi Normal University,Xi'an 710062,China

2024

电子学报(英文)

电子学报(英文)

CSTPCDEI
ISSN:1022-4653
年,卷(期):2024.33(6)