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Sign Languages are rich multi-channel languages, requiring articulation ofboth manual (hands) and non-manual (face and body) features in a precise,intricate manner. Sign Language Production (SLP), the automatic translationfrom spoken to sign languages, must embody this full sign morphology to betruly understandable by the Deaf community. Previous work has mainly focused onmanual feature production, with an under-articulated output caused byregression to the mean.  In this paper, we propose an Adversarial Multi-Channel approach to SLP. Weframe sign production as a minimax game between a transformer-based Generatorand a conditional Discriminator. Our adversarial discriminator evaluates therealism of sign production conditioned on the source text, pushing thegenerator towards a realistic and articulate output. Additionally, we fullyencapsulate sign articulators with the inclusion of non-manual features,producing facial features and mouthing patterns.  We evaluate on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T)dataset, and report state-of-the art SLP back-translation performance formanual production. We set new benchmarks for the production of multi-channelsign to underpin future research into realistic SLP.

Adversarial Training for Multi-Channel Sign Language Production