mlsanityData to 7 Oct 2026

Higher-Order Implicit Fairing Networks for 3D Human Pose Estimation

arXiv 2111.00950 ↗First posted 1 Nov 2021cs.CVcited by 8

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Abstract (arXiv)

Estimating a 3D human pose has proven to be a challenging task, primarily because of the complexity of the human body joints, occlusions, and variability in lighting conditions. In this paper, we introduce a higher-order graph convolutional framework with initial residual connections for 2D-to-3D pose estimation. Using multi-hop neighborhoods for node feature aggregation, our model is able to capture the long-range dependencies between body joints. Moreover, our approach leverages residual connections, which are integrated by design in our network architecture, ensuring that the learned feature representations retain important information from the initial features of the input layer as the network depth increases. Experiments and ablations studies conducted on two standard benchmarks demonstrate the effectiveness of our model, achieving superior performance over strong baseline methods for 3D human pose estimation.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 26 of its 39 references that are in the map. First posted on arXiv 1 Nov 2021.

Builds on

26 of 39 references.

References · 26

  1. 2017Mask R-CNNcited by 7,259
  2. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  3. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  4. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  5. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  6. 2020Simple and Deep Graph Convolutional Networkscited by 857
  7. 2016Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervisioncited by 493
  8. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 442
  9. 2017A simple yet effective baseline for 3d human pose estimationcited by 401
  10. 20183D human pose estimation in video with temporal convolutions and semi-supervised trainingcited by 401
  11. 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
  12. 2019PairNorm: Tackling Oversmoothing in GNNscited by 297
14 more
  1. 2016Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Posecited by 220
  2. 2019Semantic Graph Convolutional Networks for 3D Human Pose Regressioncited by 204
  3. 2017CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filterscited by 203
  4. 20193D Hand Shape and Pose Estimation from a Single RGB Imagecited by 174
  5. 2017Compositional Human Pose Regressioncited by 124
  6. 2017Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approachcited by 114
  7. 2018Ordinal Depth Supervision for 3D Human Pose Estimationcited by 107
  8. 2017Exploiting temporal information for 3D pose estimationcited by 89
  9. 20183D Human Pose Estimation in the Wild by Adversarial Learningcited by 86
  10. 2019Monocular 3D Human Pose Estimation by Generation and Ordinal Rankingcited by 76
  11. 2019In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representationscited by 48
  12. 2020Weakly Supervised Generative Network for Multiple 3D Human Pose Hypothesescited by 25
  13. 2019DFNets: Spectral CNNs for Graphs with Feedback-Looped Filterscited by 10
  14. 20213D Human Pose Regression using Graph Convolutional Networkcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 2; 2026: 1.

8 papers.

  1. 2023Regular Splitting Graph Network for 3D Human Pose Estimationcited by 11
  2. 2023Iterative Graph Filtering Network for 3D Human Pose Estimationcited by 1
  3. 2023Spatio-temporal MLP-graph network for 3D human pose estimationcited by 1
  4. 2024Multi-hop graph transformer network for 3D human pose estimationcited by 1
  5. 2024Flexible graph convolutional network for 3D human pose estimationcited by 1
  6. 2026CIGPose: Causal Intervention Graph Neural Network for Whole-Body Pose Estimationcited by 1
  7. 20253D Human Pose Estimation via Spatial Graph Order Attention and Temporal Body Aware Transformercited by 0
  8. 2025Adaptive graph Kolmogorov-Arnold network for 3D human pose estimationcited by 0