GNM頭部：人類頭部之生成式人體計測模型

摘要

人體頭部的參數化模型是傳統上應用於計算機視覺與圖形學中動畫、渲染及重建的重要工具。近年來，這些模型更成為生成式大型視覺模型中的關鍵條件信號，得以對生成圖像進行精確的空間控制。然而，現有的公開模型通常在解剖學範圍上有所局限，僅能建構外部幾何形狀，而忽略了口腔內部及眼部結構，且常因輸入數據集保真度不足導致幾何品質下降。本報告提出一種名為「生成式人體測量模型」（Generative Anthropometric Model, GNM）的新型參數化模型，其名稱與「人類基因組」（human genome）形成同音對應。GNM涵蓋頭部、臉部、頸部、眼球、牙齒及舌頭，並建立於包含高解析度3D掃描及優質解剖學專家手繪樣本的大型資料庫之上。本報告詳述數據來源、模型架構（包括專為眼部及口腔內部結構設計的子模型），並展示其在3D臉部掃描配準任務中的最先進效能。為促進學術社群創新，完整的GNM框架將公開釋出。

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.