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Sign languages are multi-channel visual languages, where signers use acontinuous 3D space to communicate.Sign Language Production (SLP), theautomatic translation from spoken to sign languages, must embody both thecontinuous articulation and full morphology of sign to be truly understandableby the Deaf community. Previous deep learning-based SLP works have producedonly a concatenation of isolated signs focusing primarily on the manualfeatures, leading to a robotic and non-expressive production.  In this work, we propose a novel Progressive Transformer architecture, thefirst SLP model to translate from spoken language sentences to continuous 3Dmulti-channel sign pose sequences in an end-to-end manner. Our transformernetwork architecture introduces a counter decoding that enables variable lengthcontinuous sequence generation by tracking the production progress over timeand predicting the end of sequence. We present extensive data augmentationtechniques to reduce prediction drift, alongside an adversarial training regimeand a Mixture Density Network (MDN) formulation to produce realistic andexpressive sign pose sequences.  We propose a back translation evaluation mechanism for SLP, presentingbenchmark quantitative results on the challenging PHOENIX14T dataset andsetting baselines for future research. We further provide a user evaluation ofour SLP model, to understand the Deaf reception of our sign pose productions.

Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks