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

📝 Original Info

• Title: A Topology-Aware Graph Convolutional Network for Human Pose Similarity and Action Quality Assessment
• ArXiv ID: 2511.01194
• Date: 2025-11-03
• Authors: 정보 없음 (원문에 저자 정보가 제공되지 않았습니다.)

📝 Abstract

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.

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