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.
@article{arxiv.2105.02742,
title = {Pose-Guided Sign Language Video GAN with Dynamic Lambda},
author = {Christopher Kissel and Christopher Kümmel and Dennis Ritter and Kristian Hildebrand},
journal= {arXiv preprint arXiv:2105.02742},
year = {2021}
}