mlsanityData to 14 Oct 2026

A Topology-Aware Graph Convolutional Network for Human Pose Similarity and Action Quality Assessment

arXiv 2511.01194 ↗First posted 3 Nov 2025cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Action Quality Assessment (AQA) requires fine-grained understanding of human motion and precise evaluation of pose similarity. This paper proposes a topology-aware Graph Convolutional Network (GCN) framework, termed GCN-PSN, which models the human skeleton as a graph to learn discriminative, topology-sensitive pose embeddings. Using a Siamese architecture trained with a contrastive regression objective, our method outperforms coordinate-based baselines and achieves competitive performance on AQA-7 and FineDiving benchmarks. Experimental results and ablation studies validate the effectiveness of leveraging skeletal topology for pose similarity and action quality assessment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 3 Nov 2025.

Builds on

14 of 24 references.

References · 14

  1. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  2. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  3. 2016NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysiscited by 686
  4. 2022FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessmentcited by 107
  5. 2021Group-aware Contrastive Regression for Action Quality Assessmentcited by 75
  6. 2022Action Quality Assessment with Temporal Parsing Transformercited by 31
  7. 2020Feedback Graph Convolutional Network for Skeleton-based Action Recognitioncited by 22
  8. 2024A Comprehensive Survey of Action Quality Assessment: Method and Benchmarkcited by 20
  9. 2025AthletePose3D: A Benchmark Dataset for 3D Human Pose Estimation and Kinematic Validation in Athletic Movementscited by 14
  10. 2024Deep learning for 3D human pose estimation and mesh recovery: A surveycited by 11
  11. 2024A Survey on 3D Egocentric Human Pose Estimationcited by 8
  12. 2025A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directionscited by 7
2 more
  1. 2025DFM: Deep Fourier Mimic for Expressive Dance Motion Learningcited by 4
  2. 2022A survey of top-down approaches for human pose estimationcited by 3

Built on by

Not cited yet.