# Flexible graph convolutional network for 3D human pose estimation

arXiv: https://arxiv.org/abs/2407.19077 · HTML: https://mlsanity.com/paper/2407.19077/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [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)
- [Integral Human Pose Regression](https://mlsanity.com/paper/1711.08229/) (2017)
- [Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose](https://mlsanity.com/paper/1611.07828/) (2016)
- [Semantic Graph Convolutional Networks for 3D Human Pose Regression](https://mlsanity.com/paper/1904.03345/) (2019)
- [Compositional Human Pose Regression](https://mlsanity.com/paper/1704.00159/) (2017)
- [Graph Stacked Hourglass Networks for 3D Human Pose Estimation](https://mlsanity.com/paper/2103.16385/) (2021)
- [Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation](https://mlsanity.com/paper/2108.07181/) (2021)
- [3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information](https://mlsanity.com/paper/1608.03075/) (2016)
- [Higher-Order Implicit Fairing Networks for 3D Human Pose Estimation](https://mlsanity.com/paper/2111.00950/) (2021)

## Built on by (most cited first, 1 of 1)

- [Adaptive graph Kolmogorov-Arnold network for 3D human pose estimation](https://mlsanity.com/paper/2511.08809/) (2025)

## Abstract (arXiv)

Although graph convolutional networks exhibit promising performance in 3D human pose estimation, their reliance on one-hop neighbors limits their ability to capture high-order dependencies among body joints, crucial for mitigating uncertainty arising from occlusion or depth ambiguity. To tackle this limitation, we introduce Flex-GCN, a flexible graph convolutional network designed to learn graph representations that capture broader global information and dependencies. At its core is the flexible graph convolution, which aggregates features from both immediate and second-order neighbors of each node, while maintaining the same time and memory complexity as the standard convolution. Our network architecture comprises residual blocks of flexible graph convolutional layers, as well as a global response normalization layer for global feature aggregation, normalization and calibration. Quantitative and qualitative results demonstrate the effectiveness of our model, achieving competitive performance on benchmark datasets.