A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis

• URL: http://arxiv.org/abs/2001.01259v1
• Date: Sun, 5 Jan 2020 15:32:35 GMT
• Title: A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis
• Authors: Arnab Karmakar, Deepak Mishra
• Abstract summary: We propose a simple yet effective pose transformation GAN by utilizing the Residual Learning method without any additional feature learning to generate a given human image in any arbitrary pose. Using effective data augmentation techniques and cleverly tuning the model, we achieve robustness in terms of illumination, occlusion, distortion and scale. We present a detailed study, both qualitative and quantitative, to demonstrate the superiority of our model over the existing methods on two large datasets.
• Score: 9.570395744724461
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generating photorealistic images of human subjects in any unseen pose have crucial applications in generating a complete appearance model of the subject. However, from a computer vision perspective, this task becomes significantly challenging due to the inability of modelling the data distribution conditioned on pose. Existing works use a complicated pose transformation model with various additional features such as foreground segmentation, human body parsing etc. to achieve robustness that leads to computational overhead. In this work, we propose a simple yet effective pose transformation GAN by utilizing the Residual Learning method without any additional feature learning to generate a given human image in any arbitrary pose. Using effective data augmentation techniques and cleverly tuning the model, we achieve robustness in terms of illumination, occlusion, distortion and scale. We present a detailed study, both qualitative and quantitative, to demonstrate the superiority of our model over the existing methods on two large datasets.

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