Summary
ProcFunc is a Python library for procedural 3D generation built on Blender, introduced by Alexander Raistrick, Karhan Kayan, and Jack Nugent (arXiv 2504.20775, April 2025). It offers easy-to-use Python functions that streamline creating, combining, analyzing, and executing procedural generation code. By composing semantic components combinatorially, ProcFunc enables large-scale, diverse training data generation. Vision-language models (VLMs) can use the library to edit procedural material and geometry code and to write new procedural code with significantly fewer coding errors. As a demonstration, the authors built a new procedural generator for indoor rooms featuring a collection of compositional procedural materials, showcasing its detail, runtime efficiency, and diversity for 3D synthetic data generation. Source code is available at https://github.com/princeton-vl/procfunc.
Paper Overview
- Field: Computer Vision
- Authors: Alexander Raistrick, Karhan Kayan, Jack Nugent
- Published: 2025-04-30
- arXiv: 2504.20775
Abstract (Translated)
We introduce ProcFunc, a library for Blender-based procedural 3D generation in Python. ProcFunc provides a library of easy-to-use Python functions, which streamline creating, combining, analyzing, and executing procedural generation code. ProcFunc makes it easy to create large-scale diverse training data, by combinatorial compositions of semantic components. Vision-language models (VLMs) can use ProcFunc to edit procedural material and geometry code and can create new procedural code with significantly fewer coding errors. Finally, as an example use case, we use ProcFunc to develop a new procedural generator of indoor rooms, which includes a collection of new compositional procedural materials. We demonstrate the detail, runtime efficiency, and diversity of this room generator, as well as its use for 3D synthetic data generation.
Resources
- Paper: https://arxiv.org/abs/2504.20775
- Source code: https://github.com/princeton-vl/procfunc
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