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arXiv cs.GR
arXiv cs.GR Research
· 3 weeks, 4 days ago • Youtian Lin, Yikang Yang, Zhanpeng Hu, Mengqi Zhou, Feihu Zhang, Xun Cao, Jiaheng Liu, Yao Yao

Procedura: Agentic 3D Modeling with Procedural Control

Briefing

Procedura is a new agentic 3D modeling framework built around the idea that a shape should be code, not just geometry. Given a text prompt, it plans an object as an assembly graph and writes a procedural program part by part, using typed, machine-checkable mates to place components instead of relying on guesswork.

That matters for developers because the output is not just a mesh: it is an editable, part-structured program with per-part materials and simulator-validated articulation. Each part only gets accepted after compile, mate, and connectivity checks pass, which should make the system more useful for mechanical assets, modular props, and any workflow where downstream edits matter.

A decoupled vision critic then diagnoses assembly problems and applies fixes one at a time, which is a notable design choice for reducing brittle end-to-end failures. The system was evaluated on P3D-Bench and MechBench-36, where it reportedly beat state-of-the-art native 3D generators and earlier 3D-code agents on judged quality, while producing the sharpest edges among the methods tested.

For game teams, the practical appeal is obvious: cleaner hard-surface assets, explicit part decomposition, and parameters that can be edited after generation. The open question is how well this approach scales to the messy, style-heavy assets common in production, but for mechanical content and kitbash-style pipelines it points toward a more controllable generation workflow.

“the only one whose output is an editable, part-structured program”

— Procedura authors · Describing the system's main differentiator
Original source
Read on arXiv cs.GR
At a glance
what
Procedura is an agentic 3D modeling framework that generates procedural, editable assemblies from text prompts.
who
Authors include Youtian Lin, Yikang Yang, Zhanpeng Hu, Mengqi Zhou, Feihu Zhang, Xun Cao, Jiaheng Liu, and Yao Yao.
when
Submitted to arXiv on 26 Aug 2026 as arXiv:2608.26238.
impact
It targets hard-surface asset creation with part decomposition, editable parameters, and validated articulation.
Signal Mixed

Promising for controllable asset generation, but still research-stage.

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