  This paper addresses the problem of diversity-aware sign language production,
where we want to give an image (or sequence) of a signer and produce another
image with the same pose but different attributes (\textit{e.g.} gender, skin
color). To this end, we extend the variational inference paradigm to include
information about the pose and the conditioning of the attributes. This
formulation improves the quality of the synthesised images. The generator
framework is presented as a UNet architecture to ensure spatial preservation of
the input pose, and we include the visual features from the variational
inference to maintain control over appearance and style. We generate each body
part with a separate decoder. This architecture allows the generator to deliver
better overall results. Experiments on the SMILE II dataset show that the
proposed model performs quantitatively better than state-of-the-art baselines
regarding diversity, per-pixel image quality, and pose estimation.
Quantitatively, it faithfully reproduces non-manual features for signers.
