Sign language machine translation has progressed substantially over the past decade, evolving from isolated sign recognition to end-to-end translation systems. Advances in pose estimation, transformer architectures, and large-scale dataset collection have driven progress, yet challenges remain. Datasets are limited com...
Ozge Mercanoglu Sincan, A. Pelykh, Edward Fish et al.· 0 citations
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...
A. Pelykh, Edward Fish, Ozge Mercanoglu Sincan et al.· 0 citations
This work introduces an alternative inspired by language-learning assessment, using an open-weight-LLM QA protocol that measures salient content preservation that aligns more closely with human rankings and is six to seven times more paraphrase-invariant than BLEU-4.
Oline Ranum, Edward Fish, Simon Hadfield et al.· 0 citations
SignBind-LLM, a modular framework that addresses limitations of sign language translation systems through three dedicated expert streams: one for continuous signing, one for fingerspelling, and one for lipreading, confirms that the gains are architectural rather than a consequence of scaling the language model.
Marshall Thomas, Edward Fish, Richard Bowden· 1 citation
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