MTR-MSE: Motion-Text Retrieval Method Based on Motion Semantics Expansion

The motion-text cross-retrieval task aims to bridge the motion and text spaces, enabling mutual retrieval between motion and language. However, existing methods suffer from limited feature extraction due to both insufficient data and inadequate feature extraction techniques, which restrict retrieval accuracy and semantic richness. To address this, we propose a Motion-Text Retrieval Method Based on Motion Semantics Expansion (MTR-MSE). We design specialized motion and text encoders to create a comprehensive shared feature space. Furthermore, recognizing the limitations of overly simplistic textual descriptions in existing datasets, we enhance motion semantics using large language models to generate more detailed and varied descriptions, thereby improving motion understanding. Experimental results demonstrate that our method achieves state-of-the-art performance, validating its effectiveness in addressing the challenges of cross-modal motion-text retrieval.