事实性至关重要：当图像生成与编辑遇见结构化视觉
Factuality Matters: When Image Generation and Editing Meet Structured Visuals
AI总结：
本文提出了一种统一模型，通过整合视觉语言模型和FLUX.1 Kontext，提升结构化视觉生成与编辑的多模态理解与事实准确性。
摘要：
尽管现代视觉生成模型在创建美观的自然图像方面表现出色，但它们在生成或编辑结构化视觉如图表、图表和数学图形时却面临困难，这些需求需要构图规划、文本渲染和多模态推理以确保事实准确性。为了解决这一问题，我们提出了对这一领域的首次全面、系统的调查，涵盖数据构建、模型训练和评估基准。首先，我们构建了一个包含130万高质量结构化图像对的大型数据集，这些数据来自可执行绘图程序，并通过链式推理注释进行增强。在此基础上，我们训练了一个统一的模型，该模型通过轻量级连接器将VLM与FLUX.1 Kontext结合，以增强多模态理解。一个三阶段的训练课程使特征对齐、知识注入和推理增强的生成得以逐步实现，进一步通过推理时的外部推理器得到加强。最后，我们引入了StructBench，一个用于生成和编辑的新基准，包含超过1700个具有挑战性的实例，以及一个配套的评估指标StructScore，该指标采用多轮问答协议来评估细粒度的事实准确性。对15个模型的评估表明，即使是最先进的闭源系统也远未令人满意。我们的模型在编辑性能方面表现强劲，推理时的推理在各种架构上都带来了持续的收益。通过发布数据集、模型和基准，我们旨在推动结构化视觉的统一多模态基础。
英文摘要：
While modern visual generation models excel at creating aesthetically pleasing natural images, they struggle with producing or editing structured visuals like charts, diagrams, and mathematical figures, which demand composition planning, text rendering, and multimodal reasoning for factual fidelity. To address this, we present the first comprehensive, systematic investigation of this domain, encompassing data construction, model training, and an evaluation benchmark. First, we construct a large-scale dataset of 1.3 million high-quality structured image pairs derived from executable drawing programs and augmented with chain-of-thought reasoning annotations. Building on it, we train a unified model that integrates a VLM with FLUX.1 Kontext via a lightweight connector for enhanced multimodal understanding. A three-stage training curriculum enables progressive feature alignment, knowledge infusion, and reasoning-augmented generation, further boosted by an external reasoner at inference time. Finally, we introduce StructBench, a novel benchmark for generation and editing with over 1,700 challenging instances, and an accompanying evaluation metric, StructScore, which employs a multi-round Q\&A protocol to assess fine-grained factual accuracy. Evaluations of 15 models reveal that even leading closed-source systems remain far from satisfactory. Our model attains strong editing performance, and inference-time reasoning yields consistent gains across diverse architectures. By releasing the dataset, model, and benchmark, we aim to advance unified multimodal foundations for structured visuals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
