# Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example

arXiv: https://arxiv.org/abs/2105.06407 · HTML: https://mlsanity.com/paper/2105.06407/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 10 of 10)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [State of the Art on Neural Rendering](https://mlsanity.com/paper/2004.03805/) (2020)
- [Differentiable Rendering: A Survey](https://mlsanity.com/paper/2006.12057/) (2020)
- [CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer](https://mlsanity.com/paper/2008.10298/) (2020)
- [Lightweight Real-time Makeup Try-on in Mobile Browsers with Tiny CNN Models for Facial Tracking](https://mlsanity.com/paper/1906.02260/) (2019)
- [Learning Long-Term Style-Preserving Blind Video Temporal Consistency](https://mlsanity.com/paper/2103.07278/) (2021)

## Built on by (most cited first, 5 of 5)

- [FICE: Text-Conditioned Fashion Image Editing With Guided GAN Inversion](https://mlsanity.com/paper/2301.02110/) (2023)
- [SCE-MAE: Selective Correspondence Enhancement with Masked Autoencoder for Self-Supervised Landmark Estimation](https://mlsanity.com/paper/2405.18322/) (2024)
- [Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers](https://mlsanity.com/paper/2205.06305/) (2022)
- [Hair Color Digitization through Imaging and Deep Inverse Graphics](https://mlsanity.com/paper/2202.03723/) (2022)
- [Towards Unified Dynamic Face Landmark Detection](https://mlsanity.com/paper/2608.10346/) (2026)

## Abstract (arXiv)

While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task. In this paper, we introduce an inverse computer graphics method for automatic makeup synthesis from a reference image, by learning a model that maps an example portrait image with makeup to the space of rendering parameters. This method can be used by artists to automatically create realistic virtual cosmetics image samples, or by consumers, to virtually try-on a makeup extracted from their favorite reference image.