# Convolutional Sketch Inversion

arXiv: https://arxiv.org/abs/1606.03073 · HTML: https://mlsanity.com/paper/1606.03073/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2016.

## Built on by (most cited first, 4 of 4)

- [CoGS: Controllable Generation and Search from Sketch and Style](https://mlsanity.com/paper/2203.09554/) (2022)
- [Deep Image Synthesis from Intuitive User Input: A Review and Perspectives](https://mlsanity.com/paper/2107.04240/) (2021)
- [CoMPaSS: Enhancing Spatial Understanding in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2412.13195/) (2024)
- [Unified Style Transfer](https://mlsanity.com/paper/2110.10481/) (2021)

## Abstract (arXiv)

In this paper, we use deep neural networks for inverting face sketches to synthesize photorealistic face images. We first construct a semi-simulated dataset containing a very large number of computer-generated face sketches with different styles and corresponding face images by expanding existing unconstrained face data sets. We then train models achieving state-of-the-art results on both computer-generated sketches and hand-drawn sketches by leveraging recent advances in deep learning such as batch normalization, deep residual learning, perceptual losses and stochastic optimization in combination with our new dataset. We finally demonstrate potential applications of our models in fine arts and forensic arts. In contrast to existing patch-based approaches, our deep-neural-network-based approach can be used for synthesizing photorealistic face images by inverting face sketches in the wild.