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We propose a novel approach for the synthesis of sign language videos usingGANs. We extend the previous work of Stoll et al. by using the human semanticparser of the Soft-Gated Warping-GAN from to produce photorealistic videosguided by region-level spatial layouts. Synthesizing target poses improvesperformance on independent and contrasting signers. Therefore, we haveevaluated our system with the highly heterogeneous MS-ASL dataset with over 200signers resulting in a SSIM of 0.893. Furthermore, we introduce a periodicweighting approach to the generator that reactivates the training and leads toquantitatively better results.

Pose-Guided Sign Language Video GAN with Dynamic Lambda