FaceScore: Benchmarking and Enhancing Face Quality in Human Generation
Zhenyi Liao, Qingsong Xie, Chen Chen, Hannan Lu, Zhijie Deng

TL;DR
This paper introduces FaceScore, a new metric for evaluating face quality in images generated by diffusion models, and demonstrates how it can be used to improve face realism in generated images.
Contribution
The paper develops FaceScore, a novel face quality metric aligned with human judgment, and uses it to enhance diffusion models for better face generation.
Findings
FaceScore aligns better with human judgments than existing metrics.
Using FaceScore, diffusion models' face quality can be significantly improved.
Extensive experiments validate the effectiveness of the proposed approach.
Abstract
Diffusion models (DMs) have achieved significant success in generating imaginative images given textual descriptions. However, they are likely to fall short when it comes to real-life scenarios with intricate details. The low-quality, unrealistic human faces in text-to-image generation are one of the most prominent issues, hindering the wide application of DMs in practice. Targeting addressing such an issue, we first assess the face quality of generations from popular pre-trained DMs with the aid of human annotators and then evaluate the alignment between existing metrics with human judgments. Observing that existing metrics can be unsatisfactory for quantifying face quality, we develop a novel metric named FaceScore (FS) by fine-tuning the widely used ImageReward on a dataset of (win, loss) face pairs cheaply crafted by an inpainting pipeline of DMs. Extensive studies reveal FS enjoys…
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Taxonomy
TopicsFace recognition and analysis
MethodsInpainting · Diffusion
