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This paper introduces USDCraft, a framework that leverages pretrained LLMs to programmatically model articulated 3D assets for simulation by grounding program generation in partial geometric evidence. By employing source geometry analysis and iterative geometric rechecking, the system reconstructs articulated USD assets with explicit physical properties, overcoming limitations of mesh-based methods with incomplete or out-of-distribution real-world data. Experiments show state-of-the-art articulation recovery and successful real-to-sim-to-real robot manipulation.
LLMs can now generate simulation-ready articulated 3D assets from incomplete scans, achieving state-of-the-art reconstruction and enabling robust real-to-sim transfer.
Geometrically faithful and functional articulated 3D assets are essential for real-to-sim robot manipulation, where policies trained in simulation must transfer to physical objects. Recent mesh-based methods learn to infer articulation from annotated 3D assets, but deployment remains challenging when real-world objects fall outside the training distribution or their meshes are incomplete or corrupted. To address these limitations, we formulate articulated asset reconstruction as programmatic modeling grounded in partial geometric evidence and introduce USDCraft, a framework in which a pretrained LLM writes and revises executable programs for simulation-ready articulated assets without task-specific training. We propose source geometry analysis, which converts the source mesh into a metric textual description that distinguishes observed surface from unknown space, and iterative geometric rechecking, which re-encodes each candidate in the same representation so that discrepancies point to program edits while unobserved regions remain open to completion. Visual feedback and physical authoring guidance complete the modeling process, which produces articulated USD assets with explicit physical properties that load into Isaac Sim without manual adjustment. Experiments demonstrate leading articulation recovery on two benchmarks and validate USDCraft's effectiveness for real-to-sim-to-real robot manipulation.
