Sign Language Generation

Sign language generation studies how to turn text, glosses, or other signals into signed motion and video, including the hands, face, and body cues that carry meaning. Common approaches use diffusion models, autoregressive motion-token models, flow matching, or retrieval and refinement, with gloss timing and phonological features providing structure and control. Systems are evaluated on datasets such as PHOENIX14T, CSL-Daily, and How2Sign using measures of motion quality, linguistic accuracy, and back-translation. A central challenge is ensuring that visually plausible movement faithfully expresses the intended signs, especially when paired data is scarce and standard metrics may miss errors in meaning or articulation.