# Real-time Hair Segmentation and Recoloring on Mobile GPUs

arXiv: https://arxiv.org/abs/1907.06740 · HTML: https://mlsanity.com/paper/1907.06740/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

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

- [Virtual Occlusions Through Implicit Depth](https://mlsanity.com/paper/2305.07014/) (2023)
- [Correcting Face Distortion in Wide-Angle Videos](https://mlsanity.com/paper/2111.09950/) (2021)
- [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)
- [Investigating Associational Biases in Inter-Model Communication of Large Generative Models](https://mlsanity.com/paper/2601.22093/) (2026)
- [FaceAtlasAR: Atlas of Facial Acupuncture Points in Augmented Reality](https://mlsanity.com/paper/2111.14755/) (2021)
- [Latents2Segments: Disentangling the Latent Space of Generative Models for Semantic Segmentation of Face Images](https://mlsanity.com/paper/2207.01871/) (2022)
- [Weak Segmentation-Guided GAN for~Realistic Color Edition](https://mlsanity.com/paper/2610.16620/) (2026)

## Abstract (arXiv)

We present a novel approach for neural network-based hair segmentation from a single camera input specifically designed for real-time, mobile application. Our relatively small neural network produces a high-quality hair segmentation mask that is well suited for AR effects, e.g. virtual hair recoloring. The proposed model achieves real-time inference speed on mobile GPUs (30-100+ FPS, depending on the device) with high accuracy. We also propose a very realistic hair recoloring scheme. Our method has been deployed in major AR application and is used by millions of users.