The study delves into the challenges of assessing naturalness in AI-generated images, introducing the AGIN database and proposing JOINT for accurate evaluation. The research highlights the impact of technical and rationality distortions on image naturalness, providing valuable insights for future developments in this field.
The proliferation of Artificial Intelligence-Generated Images (AGIs) has expanded the Image Naturalness Assessment (INA) problem. Different from traditional definitions, INA on AI-generated images faces diverse contents affected by technical and rationality distortions.
The AGIN database collects human opinions on overall naturalness, technical, and rationality perspectives to understand how these factors influence visual naturalness. The proposed JOINT model significantly outperforms baselines in providing consistent results for naturalness assessment.
Overall, this study contributes to understanding human reasoning in visual naturalness evaluation for AI-generated images through a multi-perspective approach.
다른 언어로
소스 콘텐츠 기반
arxiv.org
더 깊은 질문