# Multi-Density Sketch-to-Image Translation Network

arXiv: https://arxiv.org/abs/2006.10649 · HTML: https://mlsanity.com/paper/2006.10649/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Text-guided Eyeglasses Manipulation with Spatial Constraints](https://mlsanity.com/paper/2304.12539/) (2023)
- [Improving Sketch Colorization using Adversarial Segmentation Consistency](https://mlsanity.com/paper/2301.08590/) (2023)
- [Region-Aware Portrait Retouching with Sparse Interactive Guidance](https://mlsanity.com/paper/2304.04017/) (2023)
- [Semi-supervised Cycle-GAN for face photo-sketch translation in the wild](https://mlsanity.com/paper/2307.10281/) (2023)
- [Guided Image-to-Image Translation by Discriminator-Generator Communication](https://mlsanity.com/paper/2303.03598/) (2023)
- [Improving Generative Adversarial Network Generalization for Facial Expression Synthesis](https://mlsanity.com/paper/2603.15648/) (2026)

## Abstract (arXiv)

Sketch-to-image (S2I) translation plays an important role in image synthesis and manipulation tasks, such as photo editing and colorization. Some specific S2I translation including sketch-to-photo and sketch-to-painting can be used as powerful tools in the art design industry. However, previous methods only support S2I translation with a single level of density, which gives less flexibility to users for controlling the input sketches. In this work, we propose the first multi-level density sketch-to-image translation framework, which allows the input sketch to cover a wide range from rough object outlines to micro structures. Moreover, to tackle the problem of noncontinuous representation of multi-level density input sketches, we project the density level into a continuous latent space, which can then be linearly controlled by a parameter. This allows users to conveniently control the densities of input sketches and generation of images. Moreover, our method has been successfully verified on various datasets for different applications including face editing, multi-modal sketch-to-photo translation, and anime colorization, providing coarse-to-fine levels of controls to these applications.