Big thanks to Jeff Brodsky of Vertex Theory for this incredible video showcasing my tool! If you haven’t seen it yet, don’t miss out, it’s a fantastic walkthrough! 🎥 youtu.be/kEQK5Sd4484?...

I created these nodes in Maya since most production rigs|animations are done there. There aren’t many production-level character rigs in Houdini.
That said, the inference math quite is simple, so I might put together a few snippets to achieve something similar in Houdini in the future. Stay tuned😉

👍 Give a like if you'd love a follow-up on adding non-linearity to linear regression! Or drop a comment to let me know what you'd like to see next.

I’m always excited to dive deeper into #MachineLearning applied to #Rigging topics and #CharacterTD

💡 Coming next: a PCA Blendshape Node
These tools are instructional—Not meant for production use but great for exploring ML in 3D animation. Feedback & contributions are welcome!

Also, in my experience, ML models are easier to implement in custom pipelines. Their inference relies on straightforward linear algebra, making them simpler implement. And since they are easy to interpret, can be integrated with post train fine-tune with artist-driven tools (edit weight and bias)

Neural networks are great for model complex, non-linear relationships. However, for simpler problems, ML's advantages in efficiency (like you mention), data requirements (they can be trained with smaller data sets), and interpretability (easier to understand how each feature affect the outputs).

3️⃣ PCA for Generating New 3D Faces (Eigenfaces for Meshes)

Use PCA to reduce the dimensionality of 3D facial data, then generate new faces by interpolating in the reduced space, and then convert it back to the original space for a brand new face.

2️⃣ K-Means for Vertex Clustering by Position and Normals

Use k-means clustering to group vertices based on spatial proximity and normal direction. This helps segment parts of a mesh automatically (e.g., head, torso, limbs).

1️⃣ Linear Regression for "Smart" Driven Keys:
Use linear regression to model the relationship between a driver (e.g., joint rotation) and a driven (e.g., blendshape weight). Instead of setting keys manually, train the relationship using example data and let the model interpolate for unseen values.

Here are some pseudo-code images.
🎯 Modular and easy to extend:
✅ Add constraints with loss functions.
✅ Assign weights to losses for flexibility.
✅ Add new types of losses dynamically.

Perfect for tasks like mesh fitting, alignment, or other approximations
#TechInnovation
#Rigging
#CharacterTD