# 3D Human Pose Regression using Graph Convolutional Network

arXiv: https://arxiv.org/abs/2105.10379 · HTML: https://mlsanity.com/paper/2105.10379/

## 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 (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [Semantic Graph Convolutional Networks for 3D Human Pose Regression](https://mlsanity.com/paper/1904.03345/) (2019)
- [HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation](https://mlsanity.com/paper/2004.00060/) (2020)
- [Ordinal Depth Supervision for 3D Human Pose Estimation](https://mlsanity.com/paper/1805.04095/) (2018)
- [V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map](https://mlsanity.com/paper/1711.07399/) (2017)
- [Exploiting temporal information for 3D pose estimation](https://mlsanity.com/paper/1711.08585/) (2017)
- [Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image](https://mlsanity.com/paper/1701.00295/) (2017)
- [3D Human Pose Estimation in RGBD Images for Robotic Task Learning](https://mlsanity.com/paper/1803.02622/) (2018)
- [3D human pose estimation from depth maps using a deep combination of poses](https://mlsanity.com/paper/1807.05389/) (2018)

## Built on by (most cited first, 8 of 8)

- [Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments](https://mlsanity.com/paper/2208.11311/) (2022)
- [Regular Splitting Graph Network for 3D Human Pose Estimation](https://mlsanity.com/paper/2305.05785/) (2023)
- [Higher-Order Implicit Fairing Networks for 3D Human Pose Estimation](https://mlsanity.com/paper/2111.00950/) (2021)
- [Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentation](https://mlsanity.com/paper/2304.12069/) (2023)
- [ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention](https://mlsanity.com/paper/2304.02147/) (2023)
- [Iterative Graph Filtering Network for 3D Human Pose Estimation](https://mlsanity.com/paper/2307.16074/) (2023)
- [PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimation](https://mlsanity.com/paper/2308.11440/) (2023)
- [Adaptive graph Kolmogorov-Arnold network for 3D human pose estimation](https://mlsanity.com/paper/2511.08809/) (2025)

## 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.