An Implicit Parametric Morphable Dental Model

Author:

Zhang Congyi1,Elgharib Mohamed2,Fox Gereon2,Gu Min3,Theobalt Christian2,Wang Wenping4

Affiliation:

1. The University of Hong Kong, Hong Kong and Max Planck Institute for Informatics, Germany

2. Max Planck Institute for Informatics, Germany

3. The University of Hong Kong, Hong Kong

4. Texas A&M University and The University of Hong Kong, Hong Kong

Abstract

3D Morphable models of the human body capture variations among subjects and are useful in reconstruction and editing applications. Current dental models use an explicit mesh scene representation and model only the teeth, ignoring the gum. In this work, we present the first parametric 3D morphable dental model for both teeth and gum. Our model uses an implicit scene representation and is learned from rigidly aligned scans. It is based on a component-wise representation for each tooth and the gum, together with a learnable latent code for each of such components. It also learns a template shape thus enabling several applications such as segmentation, interpolation and tooth replacement. Our reconstruction quality is on par with the most advanced global implicit representations while enabling novel applications. The code will be available at https://github.com/cong-yi/DMM

Funder

GRF grants from the RGC of Hong Kong

Seed Fund for Basic Research

ERC Consolidator Grant 4DRepLy

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

Reference58 articles.

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