# Edit-As-Act: Goal-Regressive Planning for Open-Vocabulary 3D Indoor Scene Editing

arXiv: https://arxiv.org/abs/2603.17583 · HTML: https://mlsanity.com/paper/2603.17583/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Builds on (most cited first, 25 of 25)

- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics](https://mlsanity.com/paper/2011.09127/) (2020)
- [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://mlsanity.com/paper/2305.15393/) (2023)
- [Holodeck: Language Guided Generation of 3D Embodied AI Environments](https://mlsanity.com/paper/2312.09067/) (2023)
- [Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models](https://mlsanity.com/paper/2303.11989/) (2023)
- [ATISS: Autoregressive Transformers for Indoor Scene Synthesis](https://mlsanity.com/paper/2110.03675/) (2021)
- [DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis](https://mlsanity.com/paper/2303.14207/) (2023)
- [SceneScape: Text-Driven Consistent Scene Generation](https://mlsanity.com/paper/2302.01133/) (2023)
- [InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior](https://mlsanity.com/paper/2402.04717/) (2024)
- [LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models](https://mlsanity.com/paper/2412.02193/) (2024)
- [I-Design: Personalized LLM Interior Designer](https://mlsanity.com/paper/2404.02838/) (2024)
- [AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes](https://mlsanity.com/paper/2312.06644/) (2023)
- [EchoScene: Indoor Scene Generation via Information Echo over Scene Graph Diffusion](https://mlsanity.com/paper/2405.00915/) (2024)
- [Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraints](https://mlsanity.com/paper/2310.03602/) (2023)
- [ControlRoom3D: Room Generation using Semantic Proxy Rooms](https://mlsanity.com/paper/2312.05208/) (2023)
- [SceneWeaver: All-in-One 3D Scene Synthesis with an Extensible and Self-Reflective Agent](https://mlsanity.com/paper/2509.20414/) (2025)
- [CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion](https://mlsanity.com/paper/2305.16283/) (2023)
- [MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse](https://mlsanity.com/paper/2503.18470/) (2025)
- [Global-Local Tree Search in VLMs for 3D Indoor Scene Generation](https://mlsanity.com/paper/2503.18476/) (2025)
- [ArtiScene: Language-Driven Artistic 3D Scene Generation Through Image Intermediary](https://mlsanity.com/paper/2506.00742/) (2025)
- [Direct Numerical Layout Generation for 3D Indoor Scene Synthesis via Spatial Reasoning](https://mlsanity.com/paper/2506.05341/) (2025)
- [FirePlace: Geometric Refinements of LLM Common Sense Reasoning for 3D Object Placement](https://mlsanity.com/paper/2503.04919/) (2025)
- [EditRoom: LLM-parameterized Graph Diffusion for Composable 3D Room Layout Editing](https://mlsanity.com/paper/2410.12836/) (2024)
- [Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning](https://mlsanity.com/paper/2509.13351/) (2025)

## Built on by (most cited first, 3 of 3)

- [MUSE: Agentic 3D Scene Authoring via Memory-Grounded Incremental Requirement Satisfaction](https://mlsanity.com/paper/2606.14168/) (2026)
- [SceneConductor: 3D Scene Generation from a Single Image with Multi-Agent Orchestration](https://mlsanity.com/paper/2606.08402/) (2026)
- [Roomer: Reflective Object-Grounded Model Editing and Repair for 3D Indoor Layout Synthesis](https://mlsanity.com/paper/2608.01973/) (2026)

## Abstract (arXiv)

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.