Abstract
The use of Generative Adversarial Networks (GAN) in image inpainting tasks where large holes in images are predicted have proven to be successful. Previous methods were tested on various texture, face, nature datasets but to our knowledge no prior work exists where masked human body parts are predicted. In this work, we use a deep generative network where a Generalized Loss Sensitive GAN (GLS-GAN) is utilized for the people inpainting problem. We show GLS-GAN performance in the human body inpainting domain.
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Acknowledgement
This work was supported by the Scientific Research Projects Commission within Bogazici University (BAP). Project No: 14504.
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Ünlü, G.E. (2019). Person Inpainting with Generative Adversarial Networks. In: Escalera, S., Ayache, S., Wan, J., Madadi, M., Güçlü, U., Baró, X. (eds) Inpainting and Denoising Challenges. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-25614-2_9
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DOI: https://doi.org/10.1007/978-3-030-25614-2_9
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