A Topology-Aware Graph Convolutional Network for Human Pose Similarity and Action Quality Assessment
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915592464760832 |
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| author | Zeng, Minmin |
| author_facet | Zeng, Minmin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01194 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Topology-Aware Graph Convolutional Network for Human Pose Similarity and Action Quality Assessment Zeng, Minmin Computer Vision and Pattern Recognition Artificial Intelligence 68T07 (Artificial neural networks and deep learning), 68U10 (Computer graphics, computational geometry) 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. |
| title | A Topology-Aware Graph Convolutional Network for Human Pose Similarity and Action Quality Assessment |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T07 (Artificial neural networks and deep learning), 68U10 (Computer graphics, computational geometry) |
| url | https://arxiv.org/abs/2511.01194 |