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Conference Paper
SC-FEGAN: Face Editing Generative Adversarial Network with User’s Sketch and Color
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- Authors
- Youngjoo Jo, Jongyoul Park
- Issue Date
- 2019-10
- Citation
- International Conference on Computer Vision (ICCV) 2019, pp.1745-1753
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- We present a novel image editing system that generates images as the user provides free-form masks, sketches and color as inputs. Our system consists of an end-to-end trainable convolutional network. In contrast to the existing methods, our system utilizes entirely free-form user input in terms of color and shape. This allows the system to respond to the user's sketch and color inputs, using them as guidelines to generate an image. In this work, we trained the network with an additional style loss, which made it possible to generate realistic results despite large portions of the image being removed. Our proposed network architecture SC-FEGAN is well suited for generating high-quality synthetic images using intuitive user inputs.
- KSP Keywords
- Convolutional networks, End to End(E2E), End-to-end trainable, Face editing, Free-form, High-quality, Image editing, Network Architecture, Synthetic image, User input, generative adversarial network