# Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment

**Authors:** Taewoo Kim, Chaeyeon Chung, Yoonseo Kim, Sunghyun Park, Kangyeol Kim, Jaegul Choo  
**arXiv:** [2208.07765](https://arxiv.org/abs/2208.07765)  
**Date:** 2022-08-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2208.07765v1/

## Key points

- The field of hairstyle transfer presents unique challenges due to the complexity and delicacy of hairstyles.
- Existing models struggle with significant pose differences between source and target images, hindering practical applications.
- HairFIT model was introduced to address this issue but falls short in preserving delicate hair textures effectively.
- A new high-performing model has been proposed that utilizes pose-aligned latent code and local style matching in StyleGAN2 latent space.
- The new model excels in transferring hairstyles even under larger pose differences while preserving local hairstyle textures effectively.
- Challenges such as processing time efficiency and handling occlusions still persist.
- The paper presents an innovative approach to hairstyle transfer, showcasing impressive results but also highlighting areas for further improvement for more robust real-world applications.

## Summary

The field of  presents unique challenges due to the complexity and delicacy of hairstyles. While recent advancements have been made in improving hair details, existing models struggle with significant pose differences between source and target images, hindering practical applications. To address this issue, HairFIT was introduced as a  model; however, it falls short in preserving delicate hair textures effectively. A new high-performing model has been proposed that utilizes  techniques and introduces a . This model explores a pose-aligned latent code for the target hair while preserving detailed textures through local style matching in the StyleGAN2 latent space. It then inpaints any occlusions present in the source image and blends both images to generate a final output. Experimental results show that this new model excels in transferring hairstyles even under larger pose differences while effectively preserving local hairstyle textures. However, challenges such as processing time efficiency and handling occlusions still persist. In conclusion, this paper presents an innovative approach to , showcasing impressive results but also highlighting areas for further improvement for more robust real-world applications.