# Content-Conditioned Generation of Stylized Free hand Sketches

arXiv: https://arxiv.org/abs/2401.04739 · HTML: https://mlsanity.com/paper/2401.04739/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 9 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Modulating early visual processing by language](https://mlsanity.com/paper/1707.00683/) (2017)
- [GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images](https://mlsanity.com/paper/2003.02567/) (2020)
- [Deep Learning for Free-Hand Sketch: A Survey](https://mlsanity.com/paper/2001.02600/) (2020)
- [Unsupervised Sketch-to-Photo Synthesis](https://mlsanity.com/paper/1909.08313/) (2019)
- [Im2Pencil: Controllable Pencil Illustration from Photographs](https://mlsanity.com/paper/1903.08682/) (2019)
- [Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition](https://mlsanity.com/paper/2311.18254/) (2023)

## Abstract (arXiv)

In recent years, the recognition of free-hand sketches has remained a popular task. However, in some special fields such as the military field, free-hand sketches are difficult to sample on a large scale. Common data augmentation and image generation techniques are difficult to produce images with various free-hand sketching styles. Therefore, the recognition and segmentation tasks in related fields are limited. In this paper, we propose a novel adversarial generative network that can accurately generate realistic free-hand sketches with various styles. We explore the performance of the model, including using styles randomly sampled from a prior normal distribution to generate images with various free-hand sketching styles, disentangling the painters' styles from known free-hand sketches to generate images with specific styles, and generating images of unknown classes that are not in the training set. We further demonstrate with qualitative and quantitative evaluations our advantages in visual quality, content accuracy, and style imitation on SketchIME.