[2607.22738] Nova3D: Code-Native Generation of Programmable 3D Assets

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arXiv:2607.22738 (cs)

[Submitted on 22 Jul 2026]

Title:Nova3D: Code-Native Generation of Programmable 3D Assets

Authors:Nimra Noor, Muhammad Bilal, Abdullah Hussain, Hassan Baig<br>View a PDF of the paper titled Nova3D: Code-Native Generation of Programmable 3D Assets, by Nimra Noor and 3 other authors

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Abstract:Current 3D generative models mostly produce a final surface: a visually strong but largely opaque mesh. Interactive 3D worlds need more than a surface. They need named parts, an assembly hierarchy, measurable constraints, local edit handles, and joints for articulation. We present Nova3D, a system that generates 3D assets as executable Blender source code; the compiled mesh, a binary glTF (GLB), is treated as the artifact, not the asset. Because the output is a program, semantic handles exist at generation time rather than being recovered afterward by segmentation or rigging. We evaluate on Nova3D-Bench, a frozen, spec-grounded benchmark of 54 items across six domains and three difficulty levels with text and image inputs, against eleven baselines in four families (mesh-native, part-structured, code-native, and CAD) plus a same-LLM ablation. Nova3D produces an executable program and a valid artifact for 54/54 items. Every asset exposes named parts organized in a parent-child assembly tree; no mesh-native, CAD, or segmentation baseline exposes either. It satisfies 51/52 prompt-stated numeric and count constraints (best baseline: 11/52), passes 14/18 blinded local edits with locality preserved in 18/18, and articulates 59 joints across 12 assets at 98.3% geometric validity, where every baseline exposes zero native joints. Its geometry is competitive: it wins the structured domains in a pairwise shape-quality tournament and is second only to the strongest mesh-native model, while conceding texture realism to baked-PBR systems. The central result is representational: code-native generation turns a generated 3D object from an opaque surface into a programmable asset that downstream systems can inspect, measure, edit, and animate.

Subjects:

Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)

Cite as:<br>arXiv:2607.22738 [cs.GR]

(or<br>arXiv:2607.22738v1 [cs.GR] for this version)

https://doi.org/10.48550/arXiv.2607.22738

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arXiv-issued DOI via DataCite

Submission history<br>From: Muhammad Bilal [view email]<br>[v1]<br>Wed, 22 Jul 2026 16:33:54 UTC (9,192 KB)

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