SceneCode: Executable World Programs for Editable Indoor Scenes with Articulated Objects

2026-05-19

AI Summary

Existing indoor scene generation methods rely on static meshes and predefined asset libraries, struggling to produce interactive, editable, and physically plausible objects. This work proposes a code-centric generative paradigm that frames scene construction as the synthesis of executable world programs: natural language prompts are automatically compiled into structured layouts and Blender Python scripts enriched with articulated joint metadata, enabling localized editing and state traceability. The approach integrates room-level agents, a plan-design-evaluate loop, five distinct code generation strategies, and an execution-guided repair mechanism, ultimately exporting simulation-ready scenes in SDF format. The resulting assets exhibit cleaner geometry and more accurate joint semantics, significantly outperforming existing methods in downstream tasks such as robotic interaction.

Abstract

Indoor scene synthesis underpins embodied AI, robotic manipulation, and simulation-based policy evaluation, where a useful scene must specify not only what the environment looks like, but also how its objects are structured. Existing pipelines, however, typically represent generated content as static meshes and inherit articulation only from curated asset libraries, which limits object-level controllability and prevents new interactable assets from being produced on demand. We address this gap by formulating physically interactable indoor scene synthesis as programmatic world generation, and present SceneCode, a framework that compiles a natural language prompt into an executable, code-driven indoor world rather than a collection of opaque meshes. A room-level agentic backbone first turns the prompt into a structured house layout and emits per-object AssetRequests through a planner--designer--critic loop. Each request is then routed to one of five code-generation strategies and converted into a synthesized part-wise Blender Python programs that are validated through an execution-guided repair-and-refine loop. The resulting programs are compiled into simulation-ready assets, and exported as SDF for physics simulation. A persistent scene-state registry links object requests, executable programs, rendered geometry, and simulation assets, turning scene assembly into a traceable and locally editable world-building process. We evaluate SceneCode across scene-level synthesis, object-level asset quality, human judgment, and downstream robot interaction. Results show that executable world programs improve prompt-faithful indoor scene generation and produce assets with cleaner mesh structure, and simulator-loadable articulation metadata. Project page: https://scene-code.github.io/.

Problem

• indoor scene synthesis
• articulated objects
• executable programs
• interactive assets
• controllability

Innovation

• programmatic scene generation
• executable world programs
• articulated object synthesis
• code-driven 3D assets
• simulation-ready indoor scenes