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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...
SignRefine is proposed, a sign language video generation model that produces comprehensible signing from 2D keypoint conditioning alone, generalizing across appearances and visual conditions, and is preferred by sign language users for visual quality and comprehensibility in more than 80% of comparisons.
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.
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.
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