MMIG-Bench: Towards Comprehensive and Explainable Evaluation of
  Multi-Modal Image Generation Models
Recent multimodal image generators such as GPT-4o, Gemini 2.0 Flash, and
Gemini 2.5 Pro excel at following complex instructions, editing images and
maintaining concept consistency. However, they are still evaluated by disjoint
toolkits: text-to-image (T2I) benchmarks that lacks multi-modal conditioning,
and customized image generation benchmarks that overlook compositional
semantics and common knowledge. We propose MMIG-Bench, a comprehensive
Multi-Modal Image Generation Benchmark that unifies these tasks by pairing
4,850 richly annotated text prompts with 1,750 multi-view reference images
across 380 subjects, spanning humans, animals, objects, and artistic styles.
MMIG-Bench is equipped with a three-level evaluation framework: (1) low-level
metrics for visual artifacts and identity preservation of objects; (2) novel
Aspect Matching Score (AMS): a VQA-based mid-level metric that delivers
fine-grained prompt-image alignment and shows strong correlation with human
judgments; and (3) high-level metrics for aesthetics and human preference.
Using MMIG-Bench, we benchmark 17 state-of-the-art models, including Gemini 2.5
Pro, FLUX, DreamBooth, and IP-Adapter, and validate our metrics with 32k human
ratings, yielding in-depth insights into architecture and data design. We will
release the dataset and evaluation code to foster rigorous, unified evaluation
and accelerate future innovations in multi-modal image generation.
68360763eccf5a50adcddade
Hang Hua
68360763eccf5a50adcddadf
Ziyun Zeng
68360763eccf5a50adcddae0
Yizhi Song
68360763eccf5a50adcddae1
Yunlong Tang
68360763eccf5a50adcddae2
Liu He
68360763eccf5a50adcddae3
Daniel Aliaga
68360763eccf5a50adcddae4
Wei Xiong
68360763eccf5a50adcddae5
Jiebo Luo
MMIG-Bench: Towards Comprehensive and Explainable Evaluation of
  Multi-Modal Image Generation Models
Recent multimodal image generators such as GPT-4o, Gemini 2.0 Flash, and
Gemini 2.5 Pro excel at following complex instructions, editing images and
maintaining concept consistency. However, they are still evaluated by disjoint
toolkits: text-to-image (T2I) benchmarks that lacks multi-modal conditioning,
and customized image generation benchmarks that overlook compositional
semantics and common knowledge. We propose MMIG-Bench, a comprehensive
Multi-Modal Image Generation Benchmark that unifies these tasks by pairing
4,850 richly annotated text prompts with 1,750 multi-view reference images
across 380 subjects, spanning humans, animals, objects, and artistic styles.
MMIG-Bench is equipped with a three-level evaluation framework: (1) low-level
metrics for visual artifacts and identity preservation of objects; (2) novel
Aspect Matching Score (AMS): a VQA-based mid-level metric that delivers
fine-grained prompt-image alignment and shows strong correlation with human
judgments; and (3) high-level metrics for aesthetics and human preference.
Using MMIG-Bench, we benchmark 17 state-of-the-art models, including Gemini 2.5
Pro, FLUX, DreamBooth, and IP-Adapter, and validate our metrics with 32k human
ratings, yielding in-depth insights into architecture and data design. We will
release the dataset and evaluation code to foster rigorous, unified evaluation
and accelerate future innovations in multi-modal image generation.
A comprehensive benchmark, MMIG-Bench, evaluates multi-modal image generators using text prompts and reference images, providing detailed insights through low-level, mid-level, and high-level metrics.
multi-modal image generators GPT-4o Gemini 2.0 Flash Gemini 2.5 Pro MMIG-Bench text-to-image (T2I) multi-view reference images Aspect Matching Score (AMS) VQA-based visual artifacts identity preservation aesthetics human preference