mlsanityData to 14 Oct 2026

3D Human Pose Regression using Graph Convolutional Network

arXiv 2105.10379 ↗First posted 21 May 2021cs.CVcited by 8

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Abstract (arXiv)

3D human pose estimation is a difficult task, due to challenges such as occluded body parts and ambiguous poses. Graph convolutional networks encode the structural information of the human skeleton in the form of an adjacency matrix, which is beneficial for better pose prediction. We propose one such graph convolutional network named PoseGraphNet for 3D human pose regression from 2D poses. Our network uses an adaptive adjacency matrix and kernels specific to neighbor groups. We evaluate our model on the Human3.6M dataset which is a standard dataset for 3D pose estimation. Our model's performance is close to the state-of-the-art, but with much fewer parameters. The model learns interesting adjacency relations between joints that have no physical connections, but are behaviorally similar.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 13 of its 18 references that are in the map. First posted on arXiv 21 May 2021.

Builds on

13 of 18 references.

References · 13

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Mask R-CNNcited by 7,277
  3. 20183D human pose estimation in video with temporal convolutions and semi-supervised trainingcited by 403
  4. 2017A simple yet effective baseline for 3d human pose estimationcited by 401
  5. 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
  6. 2019Semantic Graph Convolutional Networks for 3D Human Pose Regressioncited by 204
  7. 2020HOPE-Net: A Graph-based Model for Hand-Object Pose Estimationcited by 112
  8. 2018Ordinal Depth Supervision for 3D Human Pose Estimationcited by 107
  9. 2017V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Mapcited by 102
  10. 2017Exploiting temporal information for 3D pose estimationcited by 89
  11. 2017Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Imagecited by 73
  12. 20183D Human Pose Estimation in RGBD Images for Robotic Task Learningcited by 33
1 more
  1. 20183D human pose estimation from depth maps using a deep combination of posescited by 6

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 5; 2024: 0; 2025: 1.

8 papers.

  1. 2022Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environmentscited by 49
  2. 2023Regular Splitting Graph Network for 3D Human Pose Estimationcited by 11
  3. 2021Higher-Order Implicit Fairing Networks for 3D Human Pose Estimationcited by 8
  4. 2023Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentationcited by 3
  5. 2023ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attentioncited by 1
  6. 2023Iterative Graph Filtering Network for 3D Human Pose Estimationcited by 1
  7. 2023PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimationcited by 0
  8. 2025Adaptive graph Kolmogorov-Arnold network for 3D human pose estimationcited by 0