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arXiv · 2511.22135

EASL: Multi-Emotion Guided Semantic Disentanglement for Expressive Sign Language Generation

Yanchao Zhao·Jihao Zhu·Yu Liu·Weizhuo Chen·Yuling Yang·Kun Peng
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Abstract

Large language models have revolutionized sign language generation by automatically transforming text into high-quality sign language videos, providing accessible communication for the Deaf community. However, existing LLM-based approaches prioritize semantic accuracy while overlooking emotional expressions, resulting in outputs that lack naturalness and expressiveness. We propose EASL (Emotion-Aware Sign Language), a multi-emotion-guided generation architecture for fine-grained emotional integration. We introduce emotion-semantic disentanglement modules with progressive training to separately extract semantic and affective features. During pose decoding, the emotional representations guide semantic interaction to generate sign poses with 7-class emotion confidence scores, enabling emotional expression recognition. Experimental results demonstrate that EASL achieves pose accuracy superior to all compared baselines by integrating multi-emotion information and effectively adapts to diffusion models to generate expressive sign language videos.

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Yanchao Zhao, Jihao Zhu, Yu Liu, Weizhuo Chen, Yuling Yang, Kun Peng. 2025-11-27. EASL: Multi-Emotion Guided Semantic Disentanglement for Expressive Sign Language Generation. https://arxiv.org/abs/2511.22135

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