Q-REAL: Towards Realism and Plausibility Evaluation for AI-Generated Content

arXiv — cs.CVMonday, November 24, 2025 at 5:00:00 AM
  • A new dataset named Q-Real has been introduced to evaluate the realism and plausibility of AI-generated images, consisting of 3,088 images annotated for major entities and judgment questions. This initiative aims to enhance the quality assessment of generative models, moving beyond the limitations of existing datasets that provide only a single quality score.
  • The development of Q-Real is significant as it provides a more nuanced approach to evaluating AI-generated content, which is essential for guiding model optimization and improving the performance of text-to-image models in various applications.
  • This advancement reflects a growing emphasis on fine-grained evaluation metrics in AI, addressing challenges such as bias in image generation and the need for diverse outputs. As AI technologies evolve, the integration of frameworks like Q-Real with other evaluation methods could lead to more robust and culturally relevant AI-generated content.
— via World Pulse Now AI Editorial System

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