AI RESEARCH
MaDiS: Taming Masked Diffusion Language Models for Sign Language Generation
arXiv CS.CV
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ArXi:2601.19577v2 Announce Type: replace Sign language generation (SLG) aims to translate written texts into expressive sign motions, bridging communication barriers for the Deaf and Hard-of-Hearing communities. Recent studies formulate SLG within the language modeling framework using autoregressive language models, which suffer from unidirectional context modeling and slow token-by-token inference. To address these limitations, we present MaDiS, a masked-diffusion-based language model for SLG that captures bidirectional dependencies and s efficient parallel multi-token generation. We further.