  Sign Language Production (SLP) is a challenging task, given the limited
resources available and the inherent diversity within sign data. As a result,
previous works have suffered from the problem of regression to the mean,
leading to under-articulated and incomprehensible signing. In this paper, we
propose using dictionary examples and a learnt codebook of facial expressions
to create expressive sign language sequences. However, simply concatenating
signs and adding the face creates robotic and unnatural sequences. To address
this we present a 7-step approach to effectively stitch sequences together.
First, by normalizing each sign into a canonical pose, cropping, and stitching
we create a continuous sequence. Then, by applying filtering in the frequency
domain and resampling each sign, we create cohesive natural sequences that
mimic the prosody found in the original data. We leverage a SignGAN model to
map the output to a photo-realistic signer and present a complete Text-to-Sign
(T2S) SLP pipeline. Our evaluation demonstrates the effectiveness of the
approach, showcasing state-of-the-art performance across all datasets. Finally,
a user evaluation shows our approach outperforms the baseline model and is
capable of producing realistic sign language sequences.
