# Flow-Guided Controllable Line Drawing Generation

arXiv: https://arxiv.org/abs/2307.07540 · HTML: https://mlsanity.com/paper/2307.07540/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 14 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Toward Multimodal Image-to-Image Translation](https://mlsanity.com/paper/1711.11586/) (2017)
- [HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing](https://mlsanity.com/paper/2111.15666/) (2021)
- [Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping](https://mlsanity.com/paper/2305.11003/) (2023)
- [Learning to generate line drawings that convey geometry and semantics](https://mlsanity.com/paper/2203.12691/) (2022)
- [Deep Learning for Free-Hand Sketch: A Survey](https://mlsanity.com/paper/2001.02600/) (2020)
- [Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence](https://mlsanity.com/paper/2005.05207/) (2020)
- [Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting](https://mlsanity.com/paper/2103.13716/) (2021)
- [Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis](https://mlsanity.com/paper/2104.05703/) (2021)
- [Im2Pencil: Controllable Pencil Illustration from Photographs](https://mlsanity.com/paper/1903.08682/) (2019)
- [PalGAN: Image Colorization with Palette Generative Adversarial Networks](https://mlsanity.com/paper/2210.11204/) (2022)
- [Quality Metric Guided Portrait Line Drawing Generation from Unpaired Training Data](https://mlsanity.com/paper/2202.03678/) (2022)

## Abstract (arXiv)

In this paper, we investigate the problem of automatically controllable artistic character line drawing generation from photographs by proposing a Vector Flow Aware and Line Controllable Image-to-Image Translation architecture, which can be viewed as an appealing intersection between Artificial Intelligence and Arts. Specifically, we first present an Image-to-Flow network (I2FNet) to efficiently and robustly create the vector flow field in a learning-based manner, which can provide a direction guide for drawing lines. Then, we introduce our well-designed Double Flow Generator (DFG) framework to fuse features from learned vector flow and input image flow guaranteeing the spatial coherence of lines. Meanwhile, in order to allow for controllable character line drawing generation, we integrate a Line Control Matrix (LCM) into DFG and train a Line Control Regressor (LCR) to synthesize drawings with different styles by elaborately controlling the level of details, such as thickness, smoothness, and continuity, of lines. Finally, we design a Fourier Transformation Loss to further constrain the character line generation from the frequency domain view of the point. Quantitative and qualitative experiments demonstrate that our approach can obtain superior performance in producing high-resolution character line-drawing images with perceptually realistic characteristics.