# Content-Consistent Generation of Realistic Eyes with Style

arXiv: https://arxiv.org/abs/1911.03346 · HTML: https://mlsanity.com/paper/1911.03346/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

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

- [Automatic Gaze Analysis: A Survey of Deep Learning based Approaches](https://mlsanity.com/paper/2108.05479/) (2021)
- [EyeNeRF: A Hybrid Representation for Photorealistic Synthesis, Animation and Relighting of Human Eyes](https://mlsanity.com/paper/2206.08428/) (2022)
- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [Unsupervised High-Resolution Portrait Gaze Correction and Animation](https://mlsanity.com/paper/2207.00256/) (2022)
- [FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning](https://mlsanity.com/paper/2209.06692/) (2022)
- [Dream, Lift, Animate: From Single Images to Animatable Gaussian Avatars](https://mlsanity.com/paper/2507.15979/) (2025)
- [BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images](https://mlsanity.com/paper/2205.01536/) (2022)

## Abstract (arXiv)

Accurately labeled real-world training data can be scarce, and hence recent works adapt, modify or generate images to boost target datasets. However, retaining relevant details from input data in the generated images is challenging and failure could be critical to the performance on the final task. In this work, we synthesize person-specific eye images that satisfy a given semantic segmentation mask (content), while following the style of a specified person from only a few reference images. We introduce two approaches, (a) one used to win the OpenEDS Synthetic Eye Generation Challenge at ICCV 2019, and (b) a principled approach to solving the problem involving simultaneous injection of style and content information at multiple scales. Our implementation is available at https://github.com/mcbuehler/Seg2Eye.