以3D空间为草稿本的可编辑文本到图像生成
3D Space as a Scratchpad for Editable Text-to-Image Generation
把文本到图像生成中的空间推理外化为可编辑的3D网格场景，再渲染回图像域作为身份保持线索，为可控生成提供了一条不依赖2D布局、支持3D编辑且编辑结果能可靠传导到最终图像的思路。
原文依据 parses subjects and background elements, instantiates them as editable 3D meshes, and employs agentic scene planning
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论大语言模型（LLM）的最新进展表明，当中间思考被外化为显式工作区（如思维链轨迹或工具增强推理）时，推理能力会得到提升。然而，视觉语言模型（VLM）缺乏用于空间推理的类似机制，这限制了其生成能够准确反映几何关系、物体身份与构图意图的图像的能力。我们引入空间草稿本这一概念——一种衔接语言意图与图像合成的3D推理基底。给定文本提示，我们的框架解析主体与背景元素，将它们实例化为可编辑的3D网格，并采用智能体式场景规划来确定放置、朝向与视角选择。所得的3D布局借助身份保持线索被渲染回图像域，使VLM能够生成空间一致且视觉连贯的输出。与先前基于2D布局的方法不同，我们的方法支持直观的3D编辑，且编辑能可靠地传导至最终图像。在实验中，该方法在GenAI-Bench上取得32%的文本对齐提升，证明了显式3D推理对于精确、可控图像生成的益处。我们的结果凸显了一种新范式：视觉语言模型不仅在语言中思考，也在空间中进行思考。代码与可视化见 https://oindrilasaha.github.io/3DScratchpad/
英文原文摘要 1431 字符
Recent progress in large language models (LLMs) has shown that reasoning improves when intermediate thoughts are externalized into explicit workspaces, such as chain-of-thought traces or tool-augmented reasoning. Yet, visual language models (VLMs) lack an analogous mechanism for spatial reasoning, limiting their ability to generate images that accurately reflect geometric relations, object identities, and compositional intent. We introduce the concept of a spatial scratchpad -- a 3D reasoning substrate that bridges linguistic intent and image synthesis. Given a text prompt, our framework parses subjects and background elements, instantiates them as editable 3D meshes, and employs agentic scene planning for placement, orientation, and viewpoint selection. The resulting 3D arrangement is rendered back into the image domain with identity-preserving cues, enabling the VLM to generate spatially consistent and visually coherent outputs. Unlike prior 2D layout-based methods, our approach supports intuitive 3D edits that propagate reliably into final images. Empirically, it achieves a 32% improvement in text alignment on GenAI-Bench, demonstrating the benefit of explicit 3D reasoning for precise, controllable image generation. Our results highlight a new paradigm for vision-language models that deliberate not only in language, but also in space. Code and visualizations at https://oindrilasaha.github.io/3DScratchpad/