GNM 头部：一种人类头部生成式人体测量模型

摘要

人体头部参数化模型是传统计算机视觉与图形学中用于动画、渲染和重建的重要工具。近年来，它们还作为生成式大型视觉模型中的关键条件信号，能够对生成图像实现精准的空间控制。然而，现有公开模型通常在解剖学范围上存在局限，仅建模外部几何结构，忽略了口腔内和眼部结构，且常因低保真度输入数据集导致几何质量下降。本报告提出一种名为生成式人体测量模型（GNM）的新型参数化模型，其名称与人类基因组同音。GNM涵盖头部、面部、颈部、眼球、牙齿和舌头，并基于大规模高分辨率三维扫描数据库及高质量解剖学专家手工样本构建。本报告详述数据来源、模型架构（包括眼部和口腔内结构的专用子模型），并展示其在三维面部扫描拟合方面的最先进性能。为促进社区创新，完整的GNM框架已公开发布。

English

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.