DDF-ISM: Internal Structure Modeling of Human Head Using Probabilistic Directed Distance Field

Abstract

The increasing interest surrounding 3D human heads for digital avatars and simulations has highlighted the need for accurate internal modeling rather than solely focusing on external approximations. Existing approaches rely on traditional optimization techniques applied to explicit 3D representations like point clouds and meshes, leading to computational inefficiencies and challenges in capturing local geometric features. To tackle these problems, we propose a novel modeling method called DDF-ISM. It leverages a probabilistic Directed Distance Field for Internal Structure Modeling, facilitating efficient and anatomically accurate deformation of different parts of the human head. DDF-ISM comprises two key components: 1) a probabilistic DDF network for implicit representation of the target model to provide crucial local geometric information, and 2) a conditioned deformation network guided by the local geometry. Additionally, we introduce a large-scale dataset of human heads with internal structures derived from high-quality Computed Tomography (CT) scans, along with well-designed template models encompassing skull, mandible, brain, and head surface. Evaluation on this dataset showcases the superiority of our approach over existing methods, exhibiting superior performance in both modeling quality and efficiency.