# Facelet-Bank for Fast Portrait Manipulation

arXiv: https://arxiv.org/abs/1803.05576 · HTML: https://mlsanity.com/paper/1803.05576/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Unsupervised High-Fidelity Facial Texture Generation and Reconstruction](https://mlsanity.com/paper/2110.04760/) (2021)
- [A comprehensive survey on semantic facial attribute editing using generative adversarial networks](https://mlsanity.com/paper/2205.10587/) (2022)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Gated SwitchGAN for multi-domain facial image translation](https://mlsanity.com/paper/2111.14096/) (2021)

## Abstract (arXiv)

Digital face manipulation has become a popular and fascinating way to touch images with the prevalence of smartphones and social networks. With a wide variety of user preferences, facial expressions, and accessories, a general and flexible model is necessary to accommodate different types of facial editing. In this paper, we propose a model to achieve this goal based on an end-to-end convolutional neural network that supports fast inference, edit-effect control, and quick partial-model update. In addition, this model learns from unpaired image sets with different attributes. Experimental results show that our framework can handle a wide range of expressions, accessories, and makeup effects. It produces high-resolution and high-quality results in fast speed.