mlsanityData to 14 Oct 2026

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

arXiv 2206.06420 ↗First posted 13 Jun 2022cs.CV cs.AI cs.LGcited by 21

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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.

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

44 of 81 references.

References · 44

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32 more
  1. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 915
  2. 2016Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervisioncited by 494
  3. 20183D human pose estimation in video with temporal convolutions and semi-supervised trainingcited by 403
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  6. 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
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  8. 20213D Human Pose Estimation with Spatial and Temporal Transformerscited by 272
  9. 2021Mesh Graphormercited by 265
  10. 2016Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Posecited by 221
  11. 2019Semantic Graph Convolutional Networks for 3D Human Pose Regressioncited by 204
  12. 2022MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Videocited by 159
  13. 2021MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimationcited by 151
  14. 2021CycleMLP: A MLP-like Architecture for Dense Predictioncited by 126
  15. 2021Transformer-Based Attention Networks for Continuous Pixel-Wise Predictioncited by 125
  16. 2017Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approachcited by 115
  17. 2021AS-MLP: An Axial Shifted MLP Architecture for Visioncited by 101
  18. 2021Graph Stacked Hourglass Networks for 3D Human Pose Estimationcited by 95
  19. 2020SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approachcited by 94
  20. 2021Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimationcited by 94
  21. 2019Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Networkcited by 92
  22. 2021PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimationcited by 89
  23. 20183D Human Pose Estimation in the Wild by Adversarial Learningcited by 86
  24. 2020Motion Guided 3D Pose Estimation from Videoscited by 85
  25. 2021Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimationcited by 62
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  27. 2020Temporal-Channel Transformer for 3D Lidar-Based Video Object Detection in Autonomous Drivingcited by 55
  28. 2021Conditional Directed Graph Convolution for 3D Human Pose Estimationcited by 52
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  32. 2019View Invariant 3D Human Pose Estimationcited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 3; 2025: 9; 2026: 1.

21 papers.

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  1. 2023Regular Splitting Graph Network for 3D Human Pose Estimationcited by 11
  2. 2023A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimationcited by 11
  3. 2023HTNet: Human Topology Aware Network for 3D Human Pose Estimationcited by 6
  4. 2025H2OT: Hierarchical Hourglass Tokenizer for Efficient Video Pose Transformerscited by 5
  5. 2023GATOR: Graph-Aware Transformer with Motion-Disentangled Regression for Human Mesh Recovery from a 2D Posecited by 4
  6. 2023Double-chain Constraints for 3D Human Pose Estimation in Images and Videoscited by 4
  7. 2025Spatial-Temporal Graph Mamba for Music-Guided Dance Video Synthesiscited by 4
  8. 2025Efficient Multi-Person Motion Prediction by Lightweight Spatial and Temporal Interactionscited by 4
  9. 2023Interweaved Graph and Attention Network for 3D Human Pose Estimationcited by 2
  10. 2024Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimationcited by 2
  11. 2025NanoHTNet: Nano Human Topology Network for Efficient 3D Human Pose Estimationcited by 2
  12. 2025FastDDHPose: Towards Unified, Efficient, and Disentangled 3D Human Pose Estimationcited by 2
9 more
  1. 2023Spatio-temporal MLP-graph network for 3D human pose estimationcited by 1
  2. 2025L3D-Pose: Lifting Pose for 3D Avatars from a Single Camera in the Wildcited by 1
  3. 2022AMPose: Alternately Mixed Global-Local Attention Model for 3D Human Pose Estimationcited by 0
  4. 2024DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning of Gough-Stewart Platformcited by 0
  5. 2024Probablistic Restoration with Adaptive Noise Sampling for 3D Human Pose Estimationcited by 0
  6. 2025PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose Estimationcited by 0
  7. 2025HyperDiff: Hypergraph Guided Diffusion Model for 3D Human Pose Estimationcited by 0
  8. 2025Adaptive graph Kolmogorov-Arnold network for 3D human pose estimationcited by 0
  9. 2026MoPO: Incorporating Motion Prior for Occluded Human Mesh Recoverycited by 0