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-v) achieves a high Spearman correlation of 0.3 with human evaluations, while the human-to-human correlation is 0.45. (2) VIESCORE (with open-source MLLM) is significantly weaker than 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：在条件图像生成这一快速发展的研究领域中,如何有效评估各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VIESCORE——一种基于视觉指令引导的可解释性指标,可用于评估任意条件图像生成任务。VIESCORE以多模态大语言模型(MLLM)的通用知识作为主干,无需训练或微调。我们在条件图像任务的七个主要方向上进行评估,发现:(1)VIESCORE(GPT4-v)与人工评估的斯皮尔曼相关系数高达0.3,而人工评估之间的一致性为0.45;(2)采用开源MLLM的VIESCORE在评估合成图像时表现明显弱于GPT-4v;(3)VIESCORE在生成类任务中达到与人工评分相当的相关性,但在编辑类任务中表现欠佳。基于上述结果,我们认为VIESCORE在图像合成任务评估中展现出替代人工评判的巨大潜力。