Towards Fine-Grained Emotion Understanding via Skeleton-Based Micro-Gesture Recognition
This work addresses the problem of fine-grained emotion understanding for applications like human-computer interaction, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackled the challenge of recognizing micro-gestures from skeleton sequences for hidden emotion understanding by enhancing the PoseC3D framework with topology-aware representations, improved temporal processing, and semantic label embeddings, achieving a Top-1 accuracy of 67.01% on the iMiGUE test set and ranking third in the MiGA Challenge.
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}.