Sign language, essential for the deaf and hard-of-hearing, presents unique challenges in translation and production due to its multimodal nature and the inherent ambiguity in mapping sign language motion to spoken language words. Previous methods often rely on gloss annotations, requiring time-intensive labor and specialized expertise in sign language. Gloss-free methods have emerged to address these limitations, but they often depend on external sign language data or dictionaries, failing to completely eliminate the need for gloss annotations. There is a clear demand for a comprehensive approach that can supplant gloss annotations and be utilized for both Sign Language Translation (SLT) and Sign Language Production (SLP). We introduce Universal Gloss-level Representation (UniGloR), a unified and self-supervised solution for both SLT and SLP, trained on multiple datasets including PHOENIX14T, How2Sign, and NIASL2021. Our results demonstrate UniGloR's effectiveness in the translation and production tasks. We further report an encouraging result for the Sign Language Recognition (SLR) on previously unseen data. Our study suggests that self-supervised learning can be made in a unified manner, paving the way for innovative and practical applications in future research.

通过引入Universal Gloss-level Representation (UniGloR)，我们提出了一个统一的自监督解决方案，用于手语翻译和手语生成任务，并在各项任务中展示了UniGloR的有效性。我们的研究表明，自监督学习可以以统一的方式进行，为未来的创新与实际应用铺平了道路。

无需术语和不必要解释的通用手语翻译和生成的亮度级别表示法