通过骨骼微动作识别，实现隐藏情绪的细粒度理解。
Towards Fine-Grained Emotion Understanding via Skeleton-Based Micro-Gesture Recognition
- 设计拓扑感知骨骼表示，捕捉细微动作模式。
- 提升时序建模能力，使动作更连贯准确。
- 引入语义标签嵌入，增强模型泛化能力。
本文针对IJCAI 2025 MiGA挑战赛，提出基于骨架序列的微动作（MGs）识别方法，以实现隐藏情绪的细粒度理解。微动作具有隐蔽性强、持续时间短、运动幅度小的特点，建模与分类极具挑战。我们以PoseC3D为基线框架，提出三项关键改进：(1) 针对iMiGUE数据集设计拓扑感知骨骼表示，更好捕捉精细运动模式；(2) 改进时序处理策略，提升运动建模的平滑性与时序一致性；(3) 引入语义标签嵌入作为辅助监督，增强模型泛化能力。在iMiGUE测试集上取得67.01%的Top-1准确率，位列官方排行榜第三。代码已开源。
原文摘要 · Abstract (English)
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{https://github.com/EGO-False-Sleep/Miga25_track1}{https://github.com/EGO-False-Sleep/Miga25\_track1}.
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