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

• URL: http://arxiv.org/abs/2511.22135v1
• Date: Thu, 27 Nov 2025 06:04:15 GMT
• Title: EASL: Multi-Emotion Guided Semantic Disentanglement for Expressive Sign Language Generation
• Authors: Yanchao Zhao, Jihao Zhu, Yu Liu, Weizhuo Chen, Yuling Yang, Kun Peng,
• Abstract summary: We propose EASL (Emotion-Aware Sign Language), a multi-emotion-guided generation architecture for fine-grained emotional integration.<n>We introduce emotion-semantic disentanglement modules with progressive training to separately extract semantic and affective features.<n>During pose decoding, the emotional representations guide semantic interaction to generate sign poses with 7-class emotion confidence scores, enabling emotional expression recognition.
• Score: 7.76229483761977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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