Sign Language Production

Sign language production (SLP) research focuses on automatically translating spoken language into realistic sign language videos, aiming to bridge communication gaps between hearing and deaf communities. Current efforts concentrate on developing sophisticated deep learning models, including transformer networks, diffusion models, and vector quantization methods, to generate continuous and expressive sign sequences from text or speech, often incorporating both manual and non-manual aspects of signing. These advancements leverage large datasets and innovative techniques like graph neural networks and knowledge distillation to improve the accuracy, fluency, and realism of generated sign language, impacting both assistive technologies and sign language research. The ultimate goal is to create robust and accessible systems for sign language translation and production.