Text-to-image (T2I) models have made substantial progress in generating
images from textual prompts. However, they frequently fail to produce images
consistent with physical commonsense, a vital capability for applications in
world simulation and everyday tasks. Current T2I evaluation benchmarks focus on
metrics such as accuracy, bias, and safety, neglecting the evaluation of
models' internal knowledge, particularly physical commonsense. To address this
issue, we introduce PhyBench, a comprehensive T2I evaluation dataset comprising
700 prompts across 4 primary categories: mechanics, optics, thermodynamics, and
material properties, encompassing 31 distinct physical scenarios. We assess 6
prominent T2I models, including proprietary models DALLE3 and Gemini, and
demonstrate that incorporating physical principles into prompts enhances the
models' ability to generate physically accurate images. Our findings reveal
that: (1) even advanced models frequently err in various physical scenarios,
except for optics; (2) GPT-4o, with item-specific scoring instructions,
effectively evaluates the models' understanding of physical commonsense,
closely aligning with human assessments; and (3) current T2I models are
primarily focused on text-to-image translation, lacking profound reasoning
regarding physical commonsense. We advocate for increased attention to the
inherent knowledge within T2I models, beyond their utility as mere image
generation tools. The code and data are available at
this https URL

通过引入 PhyBench 评估数据集，我们评估了 6 个主要的 T2I 模型，发现当前的 T2I 模型主要关注文本到图像翻译，缺乏对物理常识的深入推理；我们提倡对 T2I 模型内在知识的更多关注，超越它们仅仅作为图像生成工具的实用性。