Edit-As-Act：面向开放词汇3D室内场景编辑的目标回归规划
Edit-As-Act: Goal-Regressive Planning for Open-Vocabulary 3D Indoor Scene Editing
AI总结：
本文提出Edit-As-Act框架，通过目标回归规划实现开放词汇3D室内场景编辑，解决传统方法在生成和物理一致性上的不足，实验表明其在多个编辑任务中表现优异。
摘要：
从自然语言编辑3D室内场景在概念上简单但技术上具有挑战性。现有开放词汇系统往往重新生成大量场景部分或依赖图像空间编辑，破坏空间结构，导致意外全局变化或物理不一致的布局。这些限制源于将编辑主要视为生成任务。我们采取不同的观点。用户指令定义了期望的世界状态，编辑应是使该状态成立的最小动作序列，同时保留其他一切。这种观点促使Edit-As-Act框架将开放词汇场景编辑视为3D空间中的目标回归规划。给定源场景和自由形式指令，Edit-As-Act预测符号目标谓词，并在EditLang中规划，这是一种受PDDL启发的动作语言，我们设计了显式前提和效果编码支持，接触、碰撞和其他几何关系。语言驱动的规划器提出动作，验证器强制目标导向、单调性和物理可行性，产生可解释且物理一致的转换。通过将推理与低级生成分离，Edit-As-Act实现了指令忠实性、语义一致性和物理合理性——三个现有范式无法同时满足的标准。在E2A-Bench基准测试中，我们的63个编辑任务覆盖9个室内环境，Edit-As-Act在所有编辑类型和场景类别上显著优于先前方法。
英文摘要：
Editing a 3D indoor scene from natural language is conceptually straightforward but technically challenging. Existing open-vocabulary systems often regenerate large portions of a scene or rely on image-space edits that disrupt spatial structure, resulting in unintended global changes or physically inconsistent layouts. These limitations stem from treating editing primarily as a generative task. We take a different view. A user instruction defines a desired world state, and editing should be the minimal sequence of actions that makes this state true while preserving everything else. This perspective motivates Edit-As-Act, a framework that performs open-vocabulary scene editing as goal-regressive planning in 3D space. Given a source scene and free-form instruction, Edit-As-Act predicts symbolic goal predicates and plans in EditLang, a PDDL-inspired action language that we design with explicit preconditions and effects encoding support, contact, collision, and other geometric relations. A language-driven planner proposes actions, and a validator enforces goal-directedness, monotonicity, and physical feasibility, producing interpretable and physically coherent transformations. By separating reasoning from low-level generation, Edit-As-Act achieves instruction fidelity, semantic consistency, and physical plausibility - three criteria that existing paradigms cannot satisfy together. On E2A-Bench, our benchmark of 63 editing tasks across 9 indoor environments, Edit-As-Act significantly outperforms prior approaches across all edit types and scene categories.
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。
