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- W4286768596 abstract "Abstract Gesture-to-gesture (G2G) translation is a very difficult task due to various poses, size of hands, location of fingers and other important factors. This task serves various applications, such as gesture synthesis, gesture recognition, etc. We propose a pose guided controllable Generative Adversarial Network (GAN). We have taken into consideration the structural similarity based gesture synthesis for conditional image to image translation. Along with this, controllable structures are also used to generate image. The proposed model learns the translational mapping from a combination of losses which are used for training the proposed GAN. The generated images are evaluated based on Peak Signal-to-Noise Ratio (PSNR), Inception Score (IS) and Frechet Inception Distance (FID) on NTU hand digit dataset and Senz3D dataset. KeywordsHand gesture-to-gesture translationPose guidanceConditional generative adversarial networkControllable structures" @default.
- W4286768596 created "2022-07-23" @default.
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- W4286768596 date "2022-01-01" @default.
- W4286768596 modified "2023-09-29" @default.
- W4286768596 title "Pose Guided Controllable Gesture to Gesture Translation" @default.
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- W4286768596 doi "https://doi.org/10.1007/978-3-031-11349-9_27" @default.
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