  Sign languages are multi-channel visual languages, where signers use a
continuous 3D space to communicate.Sign Language Production (SLP), the
automatic translation from spoken to sign languages, must embody both the
continuous articulation and full morphology of sign to be truly understandable
by the Deaf community. Previous deep learning-based SLP works have produced
only a concatenation of isolated signs focusing primarily on the manual
features, leading to a robotic and non-expressive production.
  In this work, we propose a novel Progressive Transformer architecture, the
first SLP model to translate from spoken language sentences to continuous 3D
multi-channel sign pose sequences in an end-to-end manner. Our transformer
network architecture introduces a counter decoding that enables variable length
continuous sequence generation by tracking the production progress over time
and predicting the end of sequence. We present extensive data augmentation
techniques to reduce prediction drift, alongside an adversarial training regime
and a Mixture Density Network (MDN) formulation to produce realistic and
expressive sign pose sequences.
  We propose a back translation evaluation mechanism for SLP, presenting
benchmark quantitative results on the challenging PHOENIX14T dataset and
setting baselines for future research. We further provide a user evaluation of
our SLP model, to understand the Deaf reception of our sign pose productions.
