以笔触而非像素思考：通过交错推理实现过程驱动的图像生成
Think in Strokes, Not Pixels: Process-Driven Image Generation via Interleaved Reasoning
AI总结：
本文提出过程驱动的图像生成方法，通过交错推理轨迹分解合成过程，通过文本规划、视觉草图、文本反思和视觉细化四个阶段，使生成过程显式、可解释且可监督。
摘要：
人类绘画图像是逐步进行的：他们规划全局布局，草图粗略草案，检查并细化细节，最重要的是每一步都基于不断演变的视觉状态。然而，统一多模态模型能否在文本-图像交错数据集上生成中间状态的链式状态？本文介绍过程驱动的图像生成，一种多步范式，将合成分解为交错推理轨迹的思考和行动。不同于单步生成，我们的方法在多个迭代中展开，每个迭代包括四个阶段：文本规划、视觉草图、文本反思和视觉细化。文本推理明确条件视觉状态如何演变，而生成的视觉中间状态反过来约束并支撑下一步的文本推理。过程驱动生成的核心挑战源于中间状态的模糊性：如何评估部分完成的图像？我们通过密集的分步监督解决这一问题，维护两个互补的约束：对于视觉中间状态，我们强制空间和语义一致性；对于文本中间状态，我们保留先前的视觉知识，同时使模型能够识别并纠正违反提示的元素。这使生成过程显式、可解释且可监督。为了验证所提方法，我们进行了各种文本到图像生成基准测试。
英文摘要：
Humans paint images incrementally: they plan a global layout, sketch a coarse draft, inspect, and refine details, and most importantly, each step is grounded in the evolving visual states. However, can unified multimodal models trained on text-image interleaved datasets also imagine the chain of intermediate states? In this paper, we introduce process-driven image generation, a multi-step paradigm that decomposes synthesis into an interleaved reasoning trajectory of thoughts and actions. Rather than generating images in a single step, our approach unfolds across multiple iterations, each consisting of 4 stages: textual planning, visual drafting, textual reflection, and visual refinement. The textual reasoning explicitly conditions how the visual state should evolve, while the generated visual intermediate in turn constrains and grounds the next round of textual reasoning. A core challenge of process-driven generation stems from the ambiguity of intermediate states: how can models evaluate each partially-complete image? We address this through dense, step-wise supervision that maintains two complementary constraints: for the visual intermediate states, we enforce the spatial and semantic consistency; for the textual intermediate states, we preserve the prior visual knowledge while enabling the model to identify and correct prompt-violating elements. This makes the generation process explicit, interpretable, and directly supervisable. To validate proposed method, we conduct experiments under various text-to-image generation benchmarks.
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。
