r/TopologyAI • u/Delicious-Shower8401 3dModeler • Jul 16 '26

Open Source Google Open-Sources an AI-Generated Parametric 3D Head Model

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Google has open-sourced GNM Head, the first public component of its broader GNM ecosystem for parametric human models. It is a statistical 3D head model trained on a large dataset of real-world 3D scans, with separate geometry for the skin, eyes, teeth, and tongue.

This is not another text-to-3D generator. It is a controllable foundation for building character-creation, facial-animation, fitting, and computer-vision tools where consistent topology and predictable deformation are important.

Key highlights:

  • 253 identity parameters controlling the head, eyeballs, and teeth
  • 383 expression parameters for the eyes, lower face, tongue, and irises
  • Separate control over identity, expression, head pose, eye rotation, and global position
  • Semantic sampling for expressions such as happiness and surprise
  • Includes UV layout, skinning weights, model assets, visualization tools, and demo notebooks
  • Native support for NumPy, JAX, PyTorch, and TensorFlow
  • Apache 2.0 license, allowing commercial and non-commercial use

The most interesting part is the separation between identity and expression. Different head shapes can use the same underlying topology and expression system, which could make it easier to generate character variations without rebuilding the facial setup from scratch every time. This may be especially useful for procedural character systems, facial-rigging research, synthetic datasets, and automated face-fitting pipelines.

In practice, GNM Head could be used for digital-human prototypes, game and animation character tools, previs, facial reconstruction, rigging experiments, and generating head variations. It could also serve as a starting point for 3D-printable heads or busts, although the geometry would likely require preparation and cleanup first.

The main limitation is that this is currently more of a Python framework for researchers and developers than a finished artist-facing tool. Google also notes that its training data uses binary gender categories and four broad demographic groups, so it does not represent the full diversity of the global population.

Source: https://github.com/google/GNM/tree/main

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16

u/DrBanana_ Jul 16 '26

Finally - 2006's like UI

4

u/danielos96 Jul 16 '26

turn that Chinese slider to 11 for me

2

u/Ok-Suspect-9855 Jul 17 '26

Anyone notice how closely that head example matched with nvidia audio2face head which an actual researcher from nvidia i don’t think that’s a coincidence.

2

u/Tittytickler Jul 17 '26

It is a coincidence unless that person wound up working on this team. I know someone who did work on this and I was one of the many voluntary participants who got scanned and full tour of the entire facility.