Multi-Dimensional Text-to-Face Image Quality Assessment Using LLM: Database and Method

With the rise of Text-to-Image (T2I) models, generating face images from text prompts has emerged as a prominent research area. However, evaluating the quality of these generated face images, particularly with respect to fine-grained facial attributes, remains a significant challenge. To address this, we introduce the Fine -grained Text-to-Face Image Quality Assessment (FineTFIQA) database, which is designed to evaluate the ability of T2I models to generate fine-grained face images. To the best of our knowledge, this database is the largest of its kind, containing 7,218 face images generated from 1,000 text prompts that cover 111 distinct facial attributes. A large group of subjects was invited to assess the quality of text-to-face images on four evaluation dimensions: perceptual quality, human likeness, attractiveness, and consistency. Additionally, we develop the Multi-Dimensional Text-to-Face Image Quality Assessment (MDTFIQA) method based on the Large Language Model (LLM), which combines both face image features and text features to evaluate generated images on all evaluation dimensions. Extensive experimental results demonstrate that traditional face image assessment methods and general image quality assessment methods are inadequate for accurately evaluating generated text-to-face images. Our method significantly outperforms these existing methods on all evaluation dimensions, proving to be an effective method for assessing the quality of generated text-to-face images.