# FaceShop: Deep Sketch-based Face Image Editing

arXiv: https://arxiv.org/abs/1804.08972 · HTML: https://mlsanity.com/paper/1804.08972/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 24 of 24)

- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (2021)
- [Bridging Global Context Interactions for High-Fidelity Image Completion](https://mlsanity.com/paper/2104.00845/) (2021)
- [Sketch Your Own GAN](https://mlsanity.com/paper/2108.02774/) (2021)
- [Rewriting Geometric Rules of a GAN](https://mlsanity.com/paper/2207.14288/) (2022)
- [Barbershop: GAN-based Image Compositing using Segmentation Masks](https://mlsanity.com/paper/2106.01505/) (2021)
- [dualFace:Two-Stage Drawing Guidance for Freehand Portrait Sketching](https://mlsanity.com/paper/2104.12297/) (2021)
- [Deep Image Synthesis from Intuitive User Input: A Review and Perspectives](https://mlsanity.com/paper/2107.04240/) (2021)
- [Enhanced Deep Animation Video Interpolation](https://mlsanity.com/paper/2206.12657/) (2022)
- [Deep Face Video Inpainting via UV Mapping](https://mlsanity.com/paper/2109.00681/) (2021)
- [DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing](https://mlsanity.com/paper/2109.10737/) (2021)
- [Sketch-guided Image Inpainting with Partial Discrete Diffusion Process](https://mlsanity.com/paper/2404.11949/) (2024)
- [Learning Geometry-aware Representations by Sketching](https://mlsanity.com/paper/2304.08204/) (2023)
- [Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars](https://mlsanity.com/paper/2202.06358/) (2022)
- [Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization](https://mlsanity.com/paper/2210.14270/) (2022)
- [CustomSketching: Sketch Concept Extraction for Sketch-based Image Synthesis and Editing](https://mlsanity.com/paper/2402.17624/) (2024)
- [Interactive Image Inpainting Using Semantic Guidance](https://mlsanity.com/paper/2201.10753/) (2022)
- [Sketch3DVE: Sketch-based 3D-Aware Scene Video Editing](https://mlsanity.com/paper/2508.13797/) (2025)
- [Diversifying Semantic Image Synthesis and Editing via Class- and Layer-wise VAEs](https://mlsanity.com/paper/2106.13416/) (2021)
- [Hierarchical Vectorization for Portrait Images](https://mlsanity.com/paper/2205.11880/) (2022)
- [Efficient Hair Style Transfer with Generative Adversarial Networks](https://mlsanity.com/paper/2210.12524/) (2022)
- [Sketch-based Video Object Localization](https://mlsanity.com/paper/2304.00450/) (2023)
- [SwipeGANSpace: Swipe-to-Compare Image Generation via Efficient Latent Space Exploration](https://mlsanity.com/paper/2404.19693/) (2024)
- [HAIFAI: Human-AI Interaction for Mental Face Reconstruction](https://mlsanity.com/paper/2412.06323/) (2024)

## Abstract (arXiv)

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.