In the rapidly advancing field of conditional image generation research, challenges such as limited explainability lie in effectively evaluating the performance and capabilities of various models. This paper introduces VIEScore, a Visual Instruction-guided Explainable metric for evaluating any conditional image generation tasks. VIEScore leverages general knowledge from Multimodal Large Language Models (MLLMs) as the backbone and does not require training or fine-tuning. We evaluate VIEScore on seven prominent tasks in conditional image tasks and found: (1) VIEScore (GPT4-o) achieves a high Spearman correlation of 0.4 with human evaluations, while the human-to-human correlation is 0.45. (2) VIEScore (with open-source MLLM) is significantly weaker than GPT-4o and GPT-4v in evaluating synthetic images. (3) VIEScore achieves a correlation on par with human ratings in the generation tasks but struggles in editing tasks. With these results, we believe VIEScore shows its great potential to replace human judges in evaluating image synthesis tasks.

翻译：在快速发展的条件图像生成研究领域，如何有效评估各类模型的性能与能力仍面临诸多挑战，其中可解释性不足尤为突出。本文提出了VIEScore，一种用于评估任意条件图像生成任务的视觉指令引导可解释性指标。VIEScore以多模态大语言模型（MLLMs）的通用知识为骨干网络，无需训练或微调。我们在条件图像任务的七个重要任务上对VIEScore进行了评估，发现：（1）VIEScore（GPT-4o）与人工评估的斯皮尔曼相关系数高达0.4，而人工评估者之间的相关系数为0.45。（2）在评估合成图像时，采用开源MLLM的VIEScore性能显著弱于GPT-4o和GPT-4v。（3）VIEScore在生成任务中达到与人工评分相当的关联度，但在编辑任务中表现欠佳。基于这些结果，我们认为VIEScore在替代人工评估图像合成任务方面展现出巨大潜力。