New SIGNER framework improves sign language generation with time-resolved conditioning

Researchers have developed SIGNER, a novel framework for sign language generation that addresses limitations in temporal grounding. By employing a time-resolved conditioning approach with a temporal-gloss condition and local temporal fusion, SIGNER ensures that generated signs accurately reflect the intended meaning and maintain correct lexical ordering. Experiments on the Phoenix-2014T and CSL-Daily datasets show that SIGNER achieves state-of-the-art performance in generating temporally grounded sign language. AI

IMPACT Improves accessibility for the deaf and hard-of-hearing community by advancing sign language generation technology.

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

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• alphaXiv
• arXiv
• CatalyzeX
• CSL-Daily
• DagsHub
• Gotit.pub
• Hugging Face
• PHOENIX-2014T
• ScienceCast
• SIGNER
• Taeryung Lee

• paper
• model release

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