# End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning

arXiv: https://arxiv.org/abs/1501.07180 · HTML: https://mlsanity.com/paper/1501.07180/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2015.

## Built on by (most cited first, 11 of 11)

- [Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition](https://mlsanity.com/paper/2103.16019/) (2021)
- [Facial-Sketch Synthesis: A New Challenge](https://mlsanity.com/paper/2112.15439/) (2021)
- [Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues](https://mlsanity.com/paper/2212.14629/) (2022)
- [Freehand Sketch Generation from Mechanical Components](https://mlsanity.com/paper/2408.05966/) (2024)
- [Unconstrained Face Sketch Synthesis via Perception-Adaptive Network and A New Benchmark](https://mlsanity.com/paper/2112.01019/) (2021)
- [Face Sketch Synthesis via Semantic-Driven Generative Adversarial Network](https://mlsanity.com/paper/2106.15121/) (2021)
- [Semi-supervised Cycle-GAN for face photo-sketch translation in the wild](https://mlsanity.com/paper/2307.10281/) (2023)
- [Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning](https://mlsanity.com/paper/2201.01592/) (2022)
- [Enabling Robots to Draw and Tell: Towards Visually Grounded Multimodal Description Generation](https://mlsanity.com/paper/2101.12338/) (2021)
- [MOST-Net: A Memory Oriented Style Transfer Network for Face Sketch Synthesis](https://mlsanity.com/paper/2202.03596/) (2022)
- [One-shot Face Sketch Synthesis in the Wild via Generative Diffusion Prior and Instruction Tuning](https://mlsanity.com/paper/2506.15312/) (2025)

## Abstract (arXiv)

Sketch-based face recognition is an interesting task in vision and multimedia research, yet it is quite challenging due to the great difference between face photos and sketches. In this paper, we propose a novel approach for photo-sketch generation, aiming to automatically transform face photos into detail-preserving personal sketches. Unlike the traditional models synthesizing sketches based on a dictionary of exemplars, we develop a fully convolutional network to learn the end-to-end photo-sketch mapping. Our approach takes whole face photos as inputs and directly generates the corresponding sketch images with efficient inference and learning, in which the architecture are stacked by only convolutional kernels of very small sizes. To well capture the person identity during the photo-sketch transformation, we define our optimization objective in the form of joint generative-discriminative minimization. In particular, a discriminative regularization term is incorporated into the photo-sketch generation, enhancing the discriminability of the generated person sketches against other individuals. Extensive experiments on several standard benchmarks suggest that our approach outperforms other state-of-the-art methods in both photo-sketch generation and face sketch verification.