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.

该研究解决了多样性感知手语生成问题，扩展了变分推断范式以包括姿势信息和属性条件，通过 UNet 架构的生成器框架和变分推断的视觉特征生成具有不同属性的手语图像，实验证明该模型在多样性、像素图像质量和姿势估计方面的表现优于现有基准模型，并忠实地再现手语非口型特征。