  Sign Languages (SL) serve as the primary mode of communication for the Deaf
and Hard of Hearing communities. Deep learning methods for SL recognition and
translation have achieved promising results. However, Sign Language Production
(SLP) poses a challenge as the generated motions must be realistic and have
precise semantic meaning. Most SLP methods rely on 2D data, which hinders their
realism. In this work, a diffusion-based SLP model is trained on a curated
large-scale dataset of 4D signing avatars and their corresponding text
transcripts. The proposed method can generate dynamic sequences of 3D avatars
from an unconstrained domain of discourse using a diffusion process formed on a
novel and anatomically informed graph neural network defined on the SMPL-X body
skeleton. Through quantitative and qualitative experiments, we show that the
proposed method considerably outperforms previous methods of SLP. This work
makes an important step towards realistic neural sign avatars, bridging the
communication gap between Deaf and hearing communities.
