# GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks

arXiv: https://arxiv.org/abs/1710.00962 · HTML: https://mlsanity.com/paper/1710.00962/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2017.

## Built on by (most cited first, 8 of 8)

- [Sparse to Dense Motion Transfer for Face Image Animation](https://mlsanity.com/paper/2109.00471/) (2021)
- [Total Generate: Cycle in Cycle Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural Scenes](https://mlsanity.com/paper/2106.10876/) (2021)
- [Bipartite Graph Reasoning GANs for Person Pose and Facial Image Synthesis](https://mlsanity.com/paper/2211.06719/) (2022)
- [Facial Expression Translation using Landmark Guided GANs](https://mlsanity.com/paper/2209.02136/) (2022)
- [Privacy-Preserving Empathy Detection in Video Interactions](https://mlsanity.com/paper/2504.10808/) (2025)
- [A Unified Architecture of Semantic Segmentation and Hierarchical Generative Adversarial Networks for Expression Manipulation](https://mlsanity.com/paper/2112.04603/) (2021)
- [Key-point Guided Deformable Image Manipulation Using Diffusion Model](https://mlsanity.com/paper/2401.08178/) (2024)
- [Improving Generative Adversarial Network Generalization for Facial Expression Synthesis](https://mlsanity.com/paper/2603.15648/) (2026)

## Abstract (arXiv)

Facial landmarks constitute the most compressed representation of faces and are known to preserve information such as pose, gender and facial structure present in the faces. Several works exist that attempt to perform high-level face-related analysis tasks based on landmarks. In contrast, in this work, an attempt is made to tackle the inverse problem of synthesizing faces from their respective landmarks. The primary aim of this work is to demonstrate that information preserved by landmarks (gender in particular) can be further accentuated by leveraging generative models to synthesize corresponding faces. Though the problem is particularly challenging due to its ill-posed nature, we believe that successful synthesis will enable several applications such as boosting performance of high-level face related tasks using landmark points and performing dataset augmentation. To this end, a novel face-synthesis method known as Gender Preserving Generative Adversarial Network (GP-GAN) that is guided by adversarial loss, perceptual loss and a gender preserving loss is presented. Further, we propose a novel generator sub-network UDeNet for GP-GAN that leverages advantages of U-Net and DenseNet architectures. Extensive experiments and comparison with recent methods are performed to verify the effectiveness of the proposed method.