# PP-GAN : Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN

arXiv: https://arxiv.org/abs/2306.13418 · HTML: https://mlsanity.com/paper/2306.13418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [Laplacian-Steered Neural Style Transfer](https://mlsanity.com/paper/1707.01253/) (2017)
- [Style-Transfer via Texture-Synthesis](https://mlsanity.com/paper/1609.03057/) (2016)
- [Geometric Style Transfer](https://mlsanity.com/paper/2007.05471/) (2020)
- [Face Sketch Synthesis with Style Transfer using Pyramid Column Feature](https://mlsanity.com/paper/2009.08679/) (2020)
- [Style Separation and Synthesis via Generative Adversarial Networks](https://mlsanity.com/paper/1811.02740/) (2018)
- [Landmark Assisted CycleGAN for Cartoon Face Generation](https://mlsanity.com/paper/1907.01424/) (2019)
- [Photo-realistic Facial Texture Transfer](https://mlsanity.com/paper/1706.04306/) (2017)
- [How Old Are You? Face Age Translation with Identity Preservation Using GANs](https://mlsanity.com/paper/1909.04988/) (2019)
- [SLGAN: Style- and Latent-guided Generative Adversarial Network for Desirable Makeup Transfer and Removal](https://mlsanity.com/paper/2009.07557/) (2020)

## Abstract (arXiv)

The objective of a style transfer is to maintain the content of an image while transferring the style of another image. However, conventional research on style transfer has a significant limitation in preserving facial landmarks, such as the eyes, nose, and mouth, which are crucial for maintaining the identity of the image. In Korean portraits, the majority of individuals wear "Gat", a type of headdress exclusively worn by men. Owing to its distinct characteristics from the hair in ID photos, transferring the "Gat" is challenging. To address this issue, this study proposes a deep learning network that can perform style transfer, including the "Gat", while preserving the identity of the face. Unlike existing style transfer approaches, the proposed method aims to preserve texture, costume, and the "Gat" on the style image. The Generative Adversarial Network forms the backbone of the proposed network. The color, texture, and intensity were extracted differently based on the characteristics of each block and layer of the pre-trained VGG-16, and only the necessary elements during training were preserved using a facial landmark mask. The head area was presented using the eyebrow area to transfer the "Gat". Furthermore, the identity of the face was retained, and style correlation was considered based on the Gram matrix. The proposed approach demonstrated superior transfer and preservation performance compared to previous studies.