Dũng Đỗ Trung | Inha University (original) (raw)

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Hao Li

Swiss Federal Institute of Technology (ETH)

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Research paper thumbnail of SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color

We present a novel image editing system that generates images as the user provides free-form mask... more We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input. Our system consist of a end-to-end trainable convolutional network. Contrary to the existing methods, our system wholly utilizes free-form user input with color and shape. This allows the system to respond to the user's sketch and color input, using it as a guideline to generate an image. In our particular work, we trained network with additional style loss [3] 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 to generate high quality synthetic image using intuitive user inputs.

Research paper thumbnail of SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color

We present a novel image editing system that generates images as the user provides free-form mask... more We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input. Our system consist of a end-to-end trainable convolutional network. Contrary to the existing methods, our system wholly utilizes free-form user input with color and shape. This allows the system to respond to the user's sketch and color input, using it as a guideline to generate an image. In our particular work, we trained network with additional style loss [3] 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 to generate high quality synthetic image using intuitive user inputs.

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