# LandmarkGAN: Synthesizing Faces from Landmarks

arXiv: https://arxiv.org/abs/2011.00269 · HTML: https://mlsanity.com/paper/2011.00269/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Progressive Disentangled Representation Learning for Fine-Grained Controllable Talking Head Synthesis](https://mlsanity.com/paper/2211.14506/) (2022)
- [Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation](https://mlsanity.com/paper/2109.10595/) (2021)
- [A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features](https://mlsanity.com/paper/2208.00788/) (2022)
- [A comprehensive survey on semantic facial attribute editing using generative adversarial networks](https://mlsanity.com/paper/2205.10587/) (2022)
- [MaskRenderer: 3D-Infused Multi-Mask Realistic Face Reenactment](https://mlsanity.com/paper/2309.05095/) (2023)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [DEMO: Disentangled Motion Latent Flow Matching for Fine-Grained Controllable Talking Portrait Synthesis](https://mlsanity.com/paper/2510.10650/) (2025)

## Abstract (arXiv)

Face synthesis is an important problem in computer vision with many applications. In this work, we describe a new method, namely LandmarkGAN, to synthesize faces based on facial landmarks as input. Facial landmarks are a natural, intuitive, and effective representation for facial expressions and orientations, which are independent from the target's texture or color and background scene. Our method is able to transform a set of facial landmarks into new faces of different subjects, while retains the same facial expression and orientation. Experimental results on face synthesis and reenactments demonstrate the effectiveness of our method.