# LinesToFacePhoto: Face Photo Generation from Lines with Conditional Self-Attention Generative Adversarial Network

arXiv: https://arxiv.org/abs/1910.08914 · HTML: https://mlsanity.com/paper/1910.08914/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2019.

## Built on by (most cited first, 23 of 23)

- [Picture that Sketch: Photorealistic Image Generation from Abstract Sketches](https://mlsanity.com/paper/2303.11162/) (2023)
- [Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis](https://mlsanity.com/paper/2104.05703/) (2021)
- [Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models](https://mlsanity.com/paper/2305.19193/) (2023)
- [DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN](https://mlsanity.com/paper/2203.02762/) (2022)
- [Facial Attribute Transformers for Precise and Robust Makeup Transfer](https://mlsanity.com/paper/2104.02894/) (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)
- [Repositioning the Subject within Image](https://mlsanity.com/paper/2401.16861/) (2024)
- [Semi-supervised reference-based sketch extraction using a contrastive learning framework](https://mlsanity.com/paper/2407.14026/) (2024)
- [Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation](https://mlsanity.com/paper/2207.09019/) (2022)
- [REMOT: A Region-to-Whole Framework for Realistic Human Motion Transfer](https://mlsanity.com/paper/2209.00475/) (2022)
- [Stylized Face Sketch Extraction via Generative Prior with Limited Data](https://mlsanity.com/paper/2403.11263/) (2024)
- [Improving Sketch Colorization using Adversarial Segmentation Consistency](https://mlsanity.com/paper/2301.08590/) (2023)
- [Locally-Focused Face Representation for Sketch-to-Image Generation Using Noise-Induced Refinement](https://mlsanity.com/paper/2411.19005/) (2024)
- [Adversarial Segmentation Loss for Sketch Colorization](https://mlsanity.com/paper/2102.06192/) (2021)
- [Make Your Brief Stroke Real and Stereoscopic: 3D-Aware Simplified Sketch to Portrait Generation](https://mlsanity.com/paper/2302.06857/) (2023)
- [High-Quality Medical Image Generation from Free-hand Sketch](https://mlsanity.com/paper/2402.00353/) (2024)
- [Style-Content Disentanglement in Language-Image Pretraining Representations for Zero-Shot Sketch-to-Image Synthesis](https://mlsanity.com/paper/2206.01661/) (2022)
- [Effect of Instance Normalization on Fine-Grained Control for Sketch-Based Face Image Generation](https://mlsanity.com/paper/2207.08072/) (2022)
- [Semantics-Preserving Sketch Embedding for Face Generation](https://mlsanity.com/paper/2211.13015/) (2022)
- [SARS: A Novel Face and Body Shape and Appearance Aware 3D Reconstruction System extends Morphable Models](https://mlsanity.com/paper/2602.09918/) (2026)
- [Component-Aware Sketch-to-Image Generation Using Self-Attention Encoding and Coordinate-Preserving Fusion](https://mlsanity.com/paper/2603.09484/) (2026)
- [SketchFaceGS: Real-Time Sketch-Driven Face Editing and Generation with Gaussian Splatting](https://mlsanity.com/paper/2604.19202/) (2026)

## Abstract (arXiv)

In this paper, we explore the task of generating photo-realistic face images from lines. Previous methods based on conditional generative adversarial networks (cGANs) have shown their power to generate visually plausible images when a conditional image and an output image share well-aligned structures. However, these models fail to synthesize face images with a whole set of well-defined structures, e.g. eyes, noses, mouths, etc., especially when the conditional line map lacks one or several parts. To address this problem, we propose a conditional self-attention generative adversarial network (CSAGAN). We introduce a conditional self-attention mechanism to cGANs to capture long-range dependencies between different regions in faces. We also build a multi-scale discriminator. The large-scale discriminator enforces the completeness of global structures and the small-scale discriminator encourages fine details, thereby enhancing the realism of generated face images. We evaluate the proposed model on the CelebA-HD dataset by two perceptual user studies and three quantitative metrics. The experiment results demonstrate that our method generates high-quality facial images while preserving facial structures. Our results outperform state-of-the-art methods both quantitatively and qualitatively.