Large Sign Language Models: Toward 3D American Sign Language Translation

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Authors: Sen Zhang, Xiaoxiao He, Di Liu, Zhaoyang Xia, Mingyu Zhao, Chaowei Tan, Vivian Sze Wing Li, Бо Лю, Dimitris Metaxas, Mubbasir Kapadia
Cited by: 0
References: 65

Abstract

We present Large Sign Language Models (LSLM), a novel framework for translating 3D American Sign Language (ASL) by leveraging Large Language Models (LLMs) as the backbone, which can benefit hearing-impaired individuals’ virtual communication. Unlike existing sign language recognition methods that rely on 2D video, our approach directly utilizes 3D sign language data to capture rich spatial, gestural, and depth information in 3D scenes. This enables more accurate and resilient translation, enhancing digital communication accessibility for the hearing-impaired community. Beyond the task of ASL translation, our work explores the integration of complex, embodied multimodal languages into the processing capabilities of LLMs, moving beyond purely text-based inputs to broaden their understanding of human communication. We investigate both direct translation from 3D gesture features to text and an instruction-guided setting where translations can be modulated by external prompts, offering greater flexibility. This work provides a foundational step toward inclusive, multimodal intelligent systems capable of understanding diverse forms of language.

Fields of Study & Subjects

Hand Gesture Recognition Systems Hearing Impairment and Communication Interactive and Immersive Displays

Publication details

Journal / source
2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Publisher
IEEE
Pages
3109–3119
Publication date
6 March 2026