Bridging the Gap Between Semantics and Reconstruction:Unifying Sign Language Translation and Production

2026-08-09

AI Summary

Existing approaches struggle to jointly address sign language translation (text-to-sign) and sign language generation (sign-to-text) due to fundamental differences in modality direction and representational form. This work proposes Uni-SLTP, a unified framework that formulates both tasks as conditional sequence generation for the first time. By designing a shared sign language tokenizer that balances semantic abstraction with motion reconstructability, continuous signing motions are discretized into a unified token space. A conditional autoregressive model is then employed to enable bidirectional mapping between text and sign. Evaluated on public benchmarks, the proposed method significantly improves motion accuracy in sign language generation while maintaining competitive performance in sign language translation.

Abstract

Recent advances in sign language (SL) research have shown a trend toward unifying multiple sign language understanding (SLU) subtasks, such as isolated sign language recognition (ISLR), continuous sign language recognition (CSLR), and sign language translation (SLT), within a single framework, leading to substantial progress. Meanwhile, sign language production (SLP), which generates sign sequences from text, has also attracted growing attention. This naturally raises an important question: can sign language understanding and production be unified within a single framework? Compared with unifying SLU subtasks, this problem is substantially more challenging. Existing SLU tasks largely share the same direction of mapping, namely from sign inputs to linguistic outputs, whereas SLT and SLP lie in opposite directions of sign-text mapping. A unified framework must therefore address two key challenges: (1) bridging the modality gap between continuous sign motions and discrete text tokens through a shared sign tokenizer that supports both linguistic abstraction and motion reconstruction; and (2) learning a single conditional autoregressive model that can take either sign or text as input and generate the corresponding target sequence in the opposite modality. To this end, we propose Uni-SLTP, a unified framework for SLT and SLP with two key components: (1) a shared sign tokenizer that converts sign sequences into discrete tokens and latent representations, capturing both semantic and reconstructive information; and (2) a unified autoregressive generation model that formulates both tasks as conditional sequence generation. Experiments on widely used public datasets show that Uni-SLTP achieves superior motion accuracy for SLP while maintaining competitive SLT performance.

Problem

• sign language translation
• sign language production
• unified framework
• modality gap
• sign-text mapping

Innovation

• sign language translation
• sign language production
• unified framework
• shared sign tokenizer
• conditional autoregressive model