# GraphMLP: A Graph MLP-Like Architecture for 3D Human Pose Estimation

arXiv: https://arxiv.org/abs/2206.06420 · HTML: https://mlsanity.com/paper/2206.06420/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 81 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 30 of 44)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [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)
- [Pay Attention to MLPs](https://mlsanity.com/paper/2105.08050/) (2021)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [ResMLP: Feedforward networks for image classification with data-efficient training](https://mlsanity.com/paper/2105.03404/) (2021)
- [3D Human Pose Estimation with Spatial and Temporal Transformers](https://mlsanity.com/paper/2103.10455/) (2021)
- [Mesh Graphormer](https://mlsanity.com/paper/2104.00272/) (2021)
- [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)
- [MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video](https://mlsanity.com/paper/2203.00859/) (2022)
- [MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2111.12707/) (2021)
- [CycleMLP: A MLP-like Architecture for Dense Prediction](https://mlsanity.com/paper/2107.10224/) (2021)
- [Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction](https://mlsanity.com/paper/2103.12091/) (2021)
- [Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach](https://mlsanity.com/paper/1704.02447/) (2017)
- [AS-MLP: An Axial Shifted MLP Architecture for Vision](https://mlsanity.com/paper/2107.08391/) (2021)
- [Graph Stacked Hourglass Networks for 3D Human Pose Estimation](https://mlsanity.com/paper/2103.16385/) (2021)

## Built on by (most cited first, 21 of 21)

- [Regular Splitting Graph Network for 3D Human Pose Estimation](https://mlsanity.com/paper/2305.05785/) (2023)
- [A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation](https://mlsanity.com/paper/2311.03312/) (2023)
- [HTNet: Human Topology Aware Network for 3D Human Pose Estimation](https://mlsanity.com/paper/2302.09790/) (2023)
- [H2OT: Hierarchical Hourglass Tokenizer for Efficient Video Pose Transformers](https://mlsanity.com/paper/2509.06956/) (2025)
- [GATOR: Graph-Aware Transformer with Motion-Disentangled Regression for Human Mesh Recovery from a 2D Pose](https://mlsanity.com/paper/2303.05652/) (2023)
- [Double-chain Constraints for 3D Human Pose Estimation in Images and Videos](https://mlsanity.com/paper/2308.05298/) (2023)
- [Spatial-Temporal Graph Mamba for Music-Guided Dance Video Synthesis](https://mlsanity.com/paper/2507.06689/) (2025)
- [Efficient Multi-Person Motion Prediction by Lightweight Spatial and Temporal Interactions](https://mlsanity.com/paper/2507.09446/) (2025)
- [Interweaved Graph and Attention Network for 3D Human Pose Estimation](https://mlsanity.com/paper/2304.14045/) (2023)
- [Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimation](https://mlsanity.com/paper/2402.02339/) (2024)
- [NanoHTNet: Nano Human Topology Network for Efficient 3D Human Pose Estimation](https://mlsanity.com/paper/2501.15763/) (2025)
- [FastDDHPose: Towards Unified, Efficient, and Disentangled 3D Human Pose Estimation](https://mlsanity.com/paper/2512.14162/) (2025)
- [Spatio-temporal MLP-graph network for 3D human pose estimation](https://mlsanity.com/paper/2308.15313/) (2023)
- [L3D-Pose: Lifting Pose for 3D Avatars from a Single Camera in the Wild](https://mlsanity.com/paper/2501.01174/) (2025)
- [AMPose: Alternately Mixed Global-Local Attention Model for 3D Human Pose Estimation](https://mlsanity.com/paper/2210.04216/) (2022)
- [DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning of Gough-Stewart Platform](https://mlsanity.com/paper/2402.09077/) (2024)
- [Probablistic Restoration with Adaptive Noise Sampling for 3D Human Pose Estimation](https://mlsanity.com/paper/2405.02114/) (2024)
- [PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose Estimation](https://mlsanity.com/paper/2506.14596/) (2025)
- [HyperDiff: Hypergraph Guided Diffusion Model for 3D Human Pose Estimation](https://mlsanity.com/paper/2508.14431/) (2025)
- [Adaptive graph Kolmogorov-Arnold network for 3D human pose estimation](https://mlsanity.com/paper/2511.08809/) (2025)
- [MoPO: Incorporating Motion Prior for Occluded Human Mesh Recovery](https://mlsanity.com/paper/2605.09856/) (2026)

## Abstract (arXiv)

Modern multi-layer perceptron (MLP) models have shown competitive results in learning visual representations without self-attention. However, existing MLP models are not good at capturing local details and lack prior knowledge of human body configurations, which limits their modeling power for skeletal representation learning. To address these issues, we propose a simple yet effective graph-reinforced MLP-Like architecture, named GraphMLP, that combines MLPs and graph convolutional networks (GCNs) in a global-local-graphical unified architecture for 3D human pose estimation. GraphMLP incorporates the graph structure of human bodies into an MLP model to meet the domain-specific demand of the 3D human pose, while allowing for both local and global spatial interactions. Furthermore, we propose to flexibly and efficiently extend the GraphMLP to the video domain and show that complex temporal dynamics can be effectively modeled in a simple way with negligible computational cost gains in the sequence length. To the best of our knowledge, this is the first MLP-Like architecture for 3D human pose estimation in a single frame and a video sequence. Extensive experiments show that the proposed GraphMLP achieves state-of-the-art performance on two datasets, i.e., Human3.6M and MPI-INF-3DHP. Code and models are available at https://github.com/Vegetebird/GraphMLP.