ArXiv

GNM Head: A Generative aNthropometric Model of the human head

2026-07-26 · 14:34 UTC ·Stylianos Ploumpis, Jan Bednarik, Gaspard Zoss... ·1 min read
Authors
Stylianos Ploumpis, Jan Bednarik, Gaspard Zoss...
Categories
cs.CV, cs.GR
arXiv
https://arxiv.org/abs/2607.23687v1
PDF
https://arxiv.org/pdf/2607.23687v1

Brief

GNM (Generative aNthropometric Model) expands parametric head modeling to include intra-oral and ocular anatomy (eyeballs, teeth, tongue) by combining large high-resolution 3D-scan datasets with artist-created anatomy samples. The report describes data provenance and specialized sub-model architectures, demonstrates state-of-the-art 3D-face-scan fitting, and releases the complete framework (arXiv 2026-07-26).

Why it matters

Generative aNthropometric Model (GNM), introduced by Stylianos Ploumpis et al. (arXiv 2026-07-26), is a parametric head model that covers head, face, neck, eyeballs, teeth, and tongue and includes specialized ocular and intra-oral sub-models.

Key details

  • GNM is built from an extensive database of high-resolution 3D scans combined with anatomy-specific artist-made samples, reports state-of-the-art performance fitting target 3D face scans, and the full framework is publicly released at https://github.com/google/GNM.
Source evidence

Abstract

Parametric models of the human head are essential tools traditionally used in computer vision and graphics for animation, rendering, and reconstruction. More recently, they serve as crucial conditioning signals within generative large vision models, allowing for tight spatial control of generated imagery. However, existing publicly available models are typically limited in anatomical scope, modeling only outer geometry while ignoring intra-oral and ocular structures, and frequently suffer from reduced geometric quality stemming from low-fidelity input datasets. In this report we introduce a new parametric model dubbed Generative aNthropometric Model (GNM), named as a homophone of the human genome. GNM encompasses the head, face, neck, eyeballs, teeth, and tongue, and it is built on an extensive database of high-resolution 3D scans combined with high-quality anatomy specific artist-made samples. This report details the data provenance, the model architecture including the specialized sub-models for the ocular and intra-oral structures, and shows its SotA performance on fitting target 3D face scans. To foster community innovation, the complete GNM framework is made publicly available.

Comment: The GNM is publicly available at: https://github.com/google/GNM