SOURCE-LINKED INTELLIGENCE
SignRefine: Adapting Foundational Video Models for Sign Language Generation
Sign language video generation demands precise hand and facial articulation, yet modern video diffusion models, trained predominantly on spoken-language video, produce artifacts that render signing unintelligible. We propose SignRefine, a sign language video generation model that produces comprehensible signing from 2D keypoint conditioning alone, generalizing across appearances and visual conditions. Our approach builds on a pretrained video diffusion transformer and introduces local adapters with spatial grounding to selectively refine hand and face regions, steering the strong base model's
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-08T09:38:22.000Z
First collected: 2026-09-20T20:22:01.598Z. This is not the publication date.