M3T: Discrete Multi-Modal Motion Tokens for Sign Language Production

2026-03-26 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision · Depth: Expert, extended

Summary

M3T (Multi-Modal Motion Tokens) is a novel sign language production framework developed by CVSSP, University of Surrey, that addresses limitations in generating grammatically obligatory non-manual features (NMFs) like mouthings and facial expressions. The system introduces SMPL-FX, an enhanced body model that integrates FLAME's 100-dimensional expression space with SMPL-X, overcoming the representational bottleneck of standard low-dimensional facial models. M3T also employs modality-specific Finite Scalar Quantization VAEs (FSQ-VAEs) for body, hands, and face, which resolve codebook collapse issues prevalent in Vector Quantized VAEs, achieving 99% utilization for facial tokens compared to 79% for VQ. An autoregressive transformer, trained on this multi-modal motion vocabulary with an auxiliary translation objective, enables semantically grounded embeddings. M3T achieves state-of-the-art sign language production quality across How2Sign, CSL-Daily, and Phoenix14T benchmarks, and demonstrates 58.3% accuracy on NMFs-CSL for signs distinguishable only by NMFs, significantly outperforming the strongest comparable pose baseline's 49.0%.

Key takeaway

For AI Scientists developing sign language production systems, prioritizing high-fidelity facial expression modeling is critical. Your current 3D body models likely lack the necessary facial parameter resolution, and standard VQ tokenization will fail to capture subtle non-manual features. Adopt models like SMPL-FX and FSQ-VAEs to ensure your systems can generate grammatically complete and expressive sign language, moving beyond manual articulation alone.

Key insights

Integrating high-dimensional facial models and collapse-free tokenization is crucial for linguistically complete sign language production.

Principles

• Non-manual features are grammatically obligatory in sign language.
• Low-dimensional facial models limit expressive sign language generation.
• Codebook collapse hinders discrete tokenization of facial expressions.

Method

SMPL-FX integrates FLAME's 100-dimensional expression space into SMPL-X. Modality-specific FSQ-VAEs tokenize body, hand, and face motion. An autoregressive transformer with an auxiliary translation objective generates multi-modal sign tokens.

In practice

• Use SMPL-FX for richer facial expression capture in 3D avatars.
• Apply FSQ-VAEs to avoid codebook collapse in low-variance data.
• Incorporate auxiliary translation objectives for semantically grounded motion embeddings.

Topics

• Sign Language Production
• Multi-Modal Motion Tokens
• Finite Scalar Quantization
• SMPL-FX Body Model
• Non-Manual Features

Best for: AI Scientist, Research Scientist, AI Researcher, Deep Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.