MV-Sign framework enhances text-to-motion for sign language production

Researchers have developed MV-Sign, a new framework designed to improve sign language production from text. The system addresses limitations in current methods, such as noisy supervision and weak text conditioning, by integrating multi-view 3D motion data and enhancing the text-to-motion conditioning pathway. MV-Sign utilizes a novel dataset, How2Sign-MV3D, created through a cross-view curation pipeline to resolve occlusions and filter irrelevant segments. The framework also incorporates a sign-aware spatio-temporal VQ-VAE for joint body-hand tokenization and keyframe-aware retrieval to improve text-to-motion alignment, achieving significant reductions in motion error compared to existing state-of-the-art methods. AI

IMPACT Improves accessibility and communication tools for the deaf and hard-of-hearing community through enhanced sign language generation.

RANK_REASON The cluster contains a research paper detailing a new framework and dataset for sign language production. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• arXiv
• ASLCitizen-3D
• How2Sign
• How2Sign-MV3D
• MM-WAuslan-MV3D
• MV-Sign
• SGNify-MV
• ST-VQVAE

• paper
• model release

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