Abstract
We propose a novel framework for simultaneously generating and manipulating the face images with desired attributes. While the state-of-the-art attribute editing techniques have achieved the impressive performance for creating realistic attribute effects, they only address the image editing problem, using the input image as the condition of model. Recently, several studies attempt to tackle both novel face generation and attribute editing problem using a single model. However, their image quality is still unsatisfactory. Our goal is to develop a single unified model that can simultaneously create and edit high quality face images with desired attributes. A key idea of our work is that we decompose the image into the latent and attribute vector in low dimensional representation, and then utilize the GANs framework for mapping the low dimensional representation to the image. In this way, we can address both the generation and editing problem by training the proposed GANs, namely Editable GAN. For qualitative and quantitative evaluations, the proposed GANs outperform recent algorithms addressing the same problem. Also, we show that our model can achieve the competitive performance with the state-of-the-art attribute editing technique in terms of attribute editing quality.
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Acknowledgement
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ICT Consilience Creative Program (IITP-2018-2017-0-01015) supervised by the IITP (Institute for Information & communications Technology Promotion), the Ministry of Science and ICT, Korea (2018-0-00207, Immersive Media Research Laboratory), the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the MSIP (NRF-2016R1A2B4016236), and ICT R&D program of MSIP/IITP. [R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding].
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Baek, K., Bang, D., Shim, H. (2019). Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneously. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11361. Springer, Cham. https://doi.org/10.1007/978-3-030-20887-5_3
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