Generative adversarial network for bidirectional mappings between synthetic and real facial image

Haoqi Gao; Koichi Ogawara

doi:10.1117/12.2572909

12 June 2020 Generative adversarial network for bidirectional mappings between synthetic and real facial image

Haoqi Gao, Koichi Ogawara

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115190J (2020) https://doi.org/10.1117/12.2572909
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

Currently, image-to-image translation tasks have been extensively investigated by Generative Adversarial Network (GAN) ¹ and Neural Style² . However, the model of the GAN can easily lead to network collapse, making the generation process free and uncontrollable. Neural Style is more dependent on the selection of features layers and loss weights. Therefore, these may cause the network model to fail to produce deterministic results we wanted, and applications are very limited. To resolve these problems, we propose an improved transfer model based on CycleGAN and an image preprocessing method for the particularity of training datasets. In this paper, we convert the synthetic face images generated by simulator into more realistic face images. Besides, this process is reversible. Depending on your tasks and goals, you can choose from synthetic images to real images or from real images to synthetic images. This is valuable and challenging in computer vision and computer graphics. In the results, we compare our model with the most recent GAN models from qualitatively and quantitatively. We demonstrate the state-of-the-art effectiveness of the proposed method for generating high-quality images. You can see more results and discussion of our model on this website https://github.com/qiqi7788/dataset-and-result.

Citation Download Citation

Haoqi Gao and Koichi Ogawara "Generative adversarial network for bidirectional mappings between synthetic and real facial image", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115190J (12 June 2020); https://doi.org/10.1117/12.2572909

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KEYWORDS

3D modeling

Neural networks

Convolution

Network architectures

Visual process modeling

Computer vision technology

Machine vision

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