ProcFunc: Function-Oriented Abstractions for Procedural 3D Generation in Python
Alexander Raistrick, Karhan Kayan, Jack Nugent, David Yan, Lingjie Mei + 6 more
TLDR
ProcFunc is a Python library for Blender-based procedural 3D generation, simplifying code creation, combination, and execution for diverse data.
Key contributions
- Introduces ProcFunc, a Python library for Blender-based procedural 3D generation.
- Streamlines creating, combining, and executing procedural generation code.
- Enables large-scale, diverse training data via combinatorial compositions.
- Assists VLMs in editing and generating procedural code with fewer errors.
Why it matters
ProcFunc simplifies complex 3D content creation in Blender, making it more accessible and efficient for developers. It addresses the critical need for large-scale, diverse synthetic training data for AI models. The library also empowers VLMs to generate procedural code with fewer errors.
Original Abstract
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. 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. Please visit https://github.com/princeton-vl/procfunc for source code.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.