ProcFunc: Function-Oriented Abstractions for Procedural 3D Generation in Python
For researchers and developers in 3D content generation and synthetic data, ProcFunc offers a practical tool to streamline procedural generation, but it is an incremental contribution as a library rather than a novel method.
ProcFunc is a Python library for Blender-based procedural 3D generation that simplifies creating, combining, and executing procedural code, enabling large-scale diverse training data generation and reducing coding errors in VLM-based editing. It includes a new procedural indoor room generator demonstrating detail, runtime efficiency, and diversity.
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.