TL;DR
SceneCode introduces a novel framework that converts natural language prompts into executable, code-driven indoor scene models with articulated objects, enhancing controllability and editability for AI and robotics applications.
Contribution
It formulates indoor scene synthesis as programmatic world generation, enabling on-demand creation of interactable assets with a traceable, editable process.
Findings
Improves prompt-faithful scene generation
Produces cleaner mesh structures
Generates assets with articulation metadata
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…
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