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This survey aims to explore and evaluate Graph Neural Network approaches for human pose estimation.

Human pose estimation based on graph neural network: survey

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This survey aims to explore and evaluate Graph Neural Network approaches for human pose estimation.
Comprehensive review of GNN-based pose estimation techniques
Analysis of methods across dimensions like graph construction and learning paradigms
Evaluation using standard benchmarks such as Human3.6M and COCO
Identification of emerging trends and limitations in the field
GNNs effectively model spatial and temporal dependencies among body joints
High computational costs and limited generalization in varied environments are critical challenges
Emerging trends point towards hybrid models and strategies to reduce dependence on annotations

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This survey reviews GNN-based human pose estimation, highlighting trends and identifying limitations in performance across environments.

Lama et al. (2026) studied this question.

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