# S2FGAN: Semantically Aware Interactive Sketch-to-Face Translation

arXiv: https://arxiv.org/abs/2011.14785 · HTML: https://mlsanity.com/paper/2011.14785/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features](https://mlsanity.com/paper/2403.07592/) (2024)
- [Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction](https://mlsanity.com/paper/2210.16721/) (2022)
- [Deep Face Restoration: A Survey](https://mlsanity.com/paper/2211.02831/) (2022)
- [CoGS: Controllable Generation and Search from Sketch and Style](https://mlsanity.com/paper/2203.09554/) (2022)
- [ISG: I can See Your Gene Expression](https://mlsanity.com/paper/2210.16728/) (2022)
- [Subjective Face Transform using Human First Impressions](https://mlsanity.com/paper/2309.15381/) (2023)

## Abstract (arXiv)

Interactive facial image manipulation attempts to edit single and multiple face attributes using a photo-realistic face and/or semantic mask as input. In the absence of the photo-realistic image (only sketch/mask available), previous methods only retrieve the original face but ignore the potential of aiding model controllability and diversity in the translation process. This paper proposes a sketch-to-image generation framework called S2FGAN, aiming to improve users' ability to interpret and flexibility of face attribute editing from a simple sketch. The proposed framework modifies the constrained latent space semantics trained on Generative Adversarial Networks (GANs). We employ two latent spaces to control the face appearance and adjust the desired attributes of the generated face. Instead of constraining the translation process by using a reference image, the users can command the model to retouch the generated images by involving the semantic information in the generation process. In this way, our method can manipulate single or multiple face attributes by only specifying attributes to be changed. Extensive experimental results on CelebAMask-HQ dataset empirically shows our superior performance and effectiveness on this task. Our method successfully outperforms state-of-the-art methods on attribute manipulation by exploiting greater control of attribute intensity.