  Sign languages are visual languages, with vocabularies as rich as their
spoken language counterparts. However, current deep-learning based Sign
Language Production (SLP) models produce under-articulated skeleton pose
sequences from constrained vocabularies and this limits applicability. To be
understandable and accepted by the deaf, an automatic SLP system must be able
to generate co-articulated photo-realistic signing sequences for large domains
of discourse.
  In this work, we tackle large-scale SLP by learning to co-articulate between
dictionary signs, a method capable of producing smooth signing while scaling to
unconstrained domains of discourse. To learn sign co-articulation, we propose a
novel Frame Selection Network (FS-Net) that improves the temporal alignment of
interpolated dictionary signs to continuous signing sequences. Additionally, we
propose SignGAN, a pose-conditioned human synthesis model that produces
photo-realistic sign language videos direct from skeleton pose. We propose a
novel keypoint-based loss function which improves the quality of synthesized
hand images.
  We evaluate our SLP model on the large-scale meineDGS (mDGS) corpus,
conducting extensive user evaluation showing our FS-Net approach improves
co-articulation of interpolated dictionary signs. Additionally, we show that
SignGAN significantly outperforms all baseline methods for quantitative
metrics, human perceptual studies and native deaf signer comprehension.
