# Content-Consistent Generation of Realistic Eyes with Style

Source: [Content-Consistent Generation of Realistic Eyes with Style](<https://www.sota2.com/research/paper/68fa66991e209131cea113de>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:54:21.040Z

Authors: Marcel B\\"uhler, Seonwook Park, Shalini De Mello, Xucong Zhang, Otmar Hilliges

Published: 2019-11-08T16:12:59.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1911.03346>)

[PDF](<https://arxiv.org/pdf/1911.03346.pdf>)