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arXiv · 2105.02742

Pose-Guided Sign Language Video GAN with Dynamic Lambda

Christopher Kissel·Christopher Kümmel·Dennis Ritter·Kristian Hildebrand
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Abstract

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.

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Christopher Kissel, Christopher Kümmel, Dennis Ritter, Kristian Hildebrand. 2021-05-06. Pose-Guided Sign Language Video GAN with Dynamic Lambda. https://arxiv.org/abs/2105.02742

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