FaceShop: Deep Sketch-based Face Image Editing

TL;DR

FaceShop introduces an interactive, real-time face image editing system that uses sketch and copy-paste inputs, leveraging a novel neural network trained on combined image completion and translation tasks for high-quality results.

Contribution

The paper presents a novel sketch-based face editing system with a unified neural network framework supporting both sketch and copy-paste modes, enabling high-quality, real-time image manipulation.

Findings

System runs in real-time for interactive editing

Neural network trained on combined tasks improves quality

Method generalizes well to real user input

Abstract

We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest. Our interface features tools to express a desired image manipulation by providing both geometry and color constraints as user-drawn strokes. As an alternative to the direct user input, our proposed system naturally supports a copy-paste mode, which allows users to edit a given image region by using parts of another exemplar image without the need of hand-drawn sketching at all. The proposed interface runs in real-time and facilitates an interactive and iterative workflow to quickly express the intended edits. Our system is based on a novel sketch domain and a convolutional neural network trained end-to-end to automatically learn to render image regions corresponding to the input strokes. To achieve high quality and semantically consistent results we train our neural network on two simultaneous tasks, namely image completion and image translation. To the best of our knowledge, we are the first to combine these two tasks in a unified framework for interactive image editing. Our results show that the proposed sketch domain, network architecture, and training procedure generalize well to real user input and enable high quality synthesis results without additional post-processing.