Abstract
We present our solution to the MiGA Challenge at IJCAI 2025, which aims to recognize micro-gestures (MGs) from skeleton sequences for the purpose of hidden emotion understanding. MGs are characterized by their subtlety, short duration, and low motion amplitude, making them particularly challenging to model and classify. We adopt PoseC3D as the baseline framework and introduce three key enhancements: (1) a topology-aware skeleton representation specifically designed for the iMiGUE dataset to better capture fine-grained motion patterns; (2) an improved temporal processing strategy that facilitates smoother and more temporally consistent motion modeling; and (3) the incorporation of semantic label embeddings as auxiliary supervision to improve the model generalization. Our method achieves a Top-1 accuracy of 67.01\% on the iMiGUE test set. As a result of these contributions, our approach ranks third on the official MiGA Challenge leaderboard. The source code is available at \href{this https URL}{this https URL\_track1}.
Abstract (translated)
我们提出了针对2025年IJCAI MiGA挑战赛的解决方案，旨在从骨骼序列中识别微手势（MGs），以理解隐藏的情绪。微手势因其细微、短暂和低幅度运动的特点而难以建模和分类。我们的方法基于PoseC3D框架，并引入了三项关键改进：(1) 一种专为iMiGUE数据集设计的拓扑感知骨骼表示，能够更好地捕捉细微的动作模式；(2) 改进的时间处理策略，有助于更平滑、时间上更加一致地建模动作；以及 (3) 引入语义标签嵌入作为辅助监督，以提高模型泛化能力。我们的方法在iMiGUE测试集上的Top-1准确率达到了67.01%。由于这些贡献，我们在官方MiGA挑战赛排行榜上排名第三。源代码可在[\href{this https URL}{此处}](\url{this https URL\_track1})获取。
URL
https://arxiv.org/abs/2506.12848
