MaDiS: Taming Masked Diffusion Language Models for Sign Language Generation

• URL: http://arxiv.org/abs/2601.19577v1
• Date: Tue, 27 Jan 2026 13:06:47 GMT
• Title: MaDiS: Taming Masked Diffusion Language Models for Sign Language Generation
• Abstract summary: We present MaDiS, a masked-diffusion-based language model for SLG that captures bidirectional and supports efficient parallel multi-token generation.<n>We also introduce a tri-level cross-modal pretraining scheme that jointly learns from token-, latent-Hearing, and 3D-space objectives.<n>MaDiS achieves superior performance across multiple metrics, including DTW error and two newly introduced metrics, SiBLEU and SiCLIP, while reducing inference latency by nearly 30%.
• Score: 78.75809158246723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 supports efficient parallel multi-token generation. We further introduce a tri-level cross-modal pretraining scheme that jointly learns from token-, latent-, and 3D physical-space objectives, leading to richer and more grounded sign representations. To accelerate model convergence in the fine-tuning stage, we design a novel unmasking strategy with temporal checkpoints, reducing the combinatorial complexity of unmasking orders by over $10^{41}$ times. In addition, a mixture-of-parts embedding layer is developed to effectively fuse information stored in different part-wise sign tokens through learnable gates and well-optimized codebooks. Extensive experiments on CSL-Daily, Phoenix-2014T, and How2Sign demonstrate that MaDiS achieves superior performance across multiple metrics, including DTW error and two newly introduced metrics, SiBLEU and SiCLIP, while reducing inference latency by nearly 30%. Code and models will be released on our project page.

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