GraFormer: Graph Convolution Transformer for 3D Pose Estimation
arXiv:2109.08364
Abstract
Exploiting relations among 2D joints plays a crucial role yet remains semi-developed in 2D-to-3D pose estimation. To alleviate this issue, we propose GraFormer, a novel transformer architecture combined with graph convolution for 3D pose estimation. The proposed GraFormer comprises two repeatedly stacked core modules, GraAttention and ChebGConv block. GraAttention enables all 2D joints to interact in global receptive field without weakening the graph structure information of joints, which introduces vital features for later modules. Unlike vanilla graph convolutions that only model the apparent relationship of joints, ChebGConv block enables 2D joints to interact in the high-order sphere, which formulates their hidden implicit relations. We empirically show the superiority of GraFormer through conducting extensive experiments across popular benchmarks. Specifically, GraFormer outperforms state of the art on Human3.6M dataset while using 18 parameters. The code is available at https://github.com/Graformer/GraFormer .
arXiv comment9 pages, 6 figures
References in corpus (11)
- Adam: A Method for Stochastic Optimization
- Semi-Supervised Classification with Graph Convolutional Networks
- Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- Embodied Hands: Modeling and Capturing Hands and Bodies Together
- Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- Stacked Hourglass Networks for Human Pose Estimation
- Compositional Human Pose Regression
- 3D Hand Shape and Pose Estimation from a Single RGB Image
- Skeleton Aware Multi-modal Sign Language Recognition
- Graph Stacked Hourglass Networks for 3D Human Pose Estimation
- Exploiting Learnable Joint Groups for Hand Pose Estimation