R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation

TL;DR

R2I-Bench is a new benchmark designed to evaluate reasoning capabilities in text-to-image generation models, revealing current models' limitations and guiding future improvements in reasoning-aware architectures.

Contribution

The paper introduces R2I-Bench, a comprehensive dataset and evaluation metric specifically targeting reasoning in text-to-image generation, filling a critical gap in current assessment methods.

Findings

Current models show limited reasoning performance.

Decoupled reasoning and generation frameworks still struggle with reasoning tasks.

The benchmark highlights the need for more reasoning-aware T2I architectures.

Abstract

Reasoning is a fundamental capability often required in real-world text-to-image (T2I) generation, e.g., generating ``a bitten apple that has been left in the air for more than a week`` necessitates understanding temporal decay and commonsense concepts. While recent T2I models have made impressive progress in producing photorealistic images, their reasoning capability remains underdeveloped and insufficiently evaluated. To bridge this gap, we introduce R2I-Bench, a comprehensive benchmark specifically designed to rigorously assess reasoning-driven T2I generation. R2I-Bench comprises meticulously curated data instances, spanning core reasoning categories, including commonsense, mathematical, logical, compositional, numerical, causal, and concept mixing. To facilitate fine-grained evaluation, we design R2IScore, a QA-style metric based on instance-specific, reasoning-oriented evaluation questions that assess three critical dimensions: text-image alignment, reasoning accuracy, and image quality. Extensive experiments with 16 representative T2I models, including a strong pipeline-based framework that decouples reasoning and generation using the state-of-the-art language and image generation models, demonstrate consistently limited reasoning performance, highlighting the need for more robust, reasoning-aware architectures in the next generation of T2I systems. Project Page: https://r2i-bench.github.io