Annotated Houdini SOPs Machine Learning Setup

Unlock a complete SOPs-based machine learning workflow for Houdini with this .hiplc file! This setup is designed to give a quick and easy introduction to machine learning in Houdini, offering everything you need—from training data to a fully annotated scene file. You’ll also gain access to an exclusive Discord channel for direct support and idea-sharing!

This workflow is a streamlined version of the one featured in my autogroom setup (as seen in this video). To be more exact the hair lenght prediction from an image. It does not give you the full autogroom tool, but rather all the building stones you need to create your own! It leverages Houdini’s power combined with machine learning techniques, giving you all the tools to start your own project.

What’s included:

• .hiplc file with a complete SOPs-based machine learning setup.
• Pre-provided training data.
• Detailed annotations to guide you through every step.
• Access to an exclusive Discord community for questions, ideas, and support.

Key Features:

• Utilizes a convolutional neural network (CNN) built in PyTorch (the only required external library).
• Integrates Houdini's native ONNX inference node for seamless importing and applying of trained networks.
• Highlights Houdini's advantages in combining procedural workflows with machine learning.

Why SOPs and not TOPs?
SOPs are the core of most Houdini workflows, and not every pipeline supports TOPs. However, this setup can be easily integrated into TOPs if needed.

Take your Houdini skills to the next level by combining procedural power with machine learning in this fully annotated setup!