用图卷积捕捉骨骼拓扑，提升动作质量评估精度
A Topology-Aware Graph Convolutional Network for Human Pose Similarity and Action Quality Assessment
- 构建拓扑感知图网络，建模骨骼结构关系
- 在AQA-7和FineDiving上超越坐标基线方法
- 适合动作分析与体育训练场景使用
动作质量评估（AQA）需要对人类运动进行细粒度理解，并精确评估姿态相似性。本文提出一种拓扑感知图卷积网络框架GCN-PSN，将人体骨骼视为图结构，学习具有判别力且对拓扑敏感的姿态嵌入。采用孪生网络结构，以对比回归目标进行训练，在AQA-7和FineDiving基准上优于基于坐标的基线方法，并取得具有竞争力的性能。实验结果与消融研究验证了利用骨骼拓扑结构对姿态相似性和动作质量评估的有效性。
原文摘要 · Abstract (English)
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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