Thinking in Pictures: A Systematic Benchmark for Reasoning-driven Image Generation

2026-09-02

AI Summary

本文针对视觉生成模型缺乏高层次推理能力的问题，提出RIG-BENCH基准，评估模型在四个认知领域中的推理驱动图像生成能力。

Abstract

Recent advancements in unified generative models (UGMs) and world simulators have achieved unprecedented results in visual perception and synthesis. However, these models primarily rely on surface-level event alignment, leaving the capacity for high-level visual reasoning underexplored. True visual generative intelligence demands "Reasoning-to-Generation", an ability to infer latent rules from visual inputs and manifest solutions through precise, logically constrained visual outcomes. We introduce RIG-BENCH, a novel comprehensive benchmark that systematically evaluates Reasoning-driven Image Generation (RIG) across four cognitively demanding domains: Concept-based, Transformation-based, Pattern & Structure, and Scenario-based. Featuring 2000 curated samples, RIG-BENCH serves as a rigorous stress test for RIG. Our extensive evaluations of state-of-the-art UGMs and image/video generation models reveal a significant reasoning-generation gap, wherein models frequently produce locally plausible but globally illogical outputs. RIG-BENCH provides a vital diagnostic framework to guide the development of next-generation, logically grounded UGMs and world simulators.

Problem

• Reasoning-driven Image Generation
• visual reasoning
• unified generative models

Innovation

• RIG-BENCH
• Reasoning-driven Image Generation
• visual reasoning
• unified generative models
• world simulators