  We propose a novel approach for the synthesis of sign language videos using
GANs. We extend the previous work of Stoll et al. by using the human semantic
parser of the Soft-Gated Warping-GAN from to produce photorealistic videos
guided by region-level spatial layouts. Synthesizing target poses improves
performance on independent and contrasting signers. Therefore, we have
evaluated our system with the highly heterogeneous MS-ASL dataset with over 200
signers resulting in a SSIM of 0.893. Furthermore, we introduce a periodic
weighting approach to the generator that reactivates the training and leads to
quantitatively better results.
