SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
This work addresses the need for more flexible and user-friendly image editing tools in computer vision, though it is incremental as it builds on existing GAN methods.
The authors tackled the problem of generating realistic face images from free-form user sketches and color inputs, achieving high-quality synthetic images that respond to intuitive user guidelines.
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 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.