Deep Deformable 3D Caricatures with Learned Shape Control

• URL: http://arxiv.org/abs/2207.14593v1
• Date: Fri, 29 Jul 2022 10:21:27 GMT
• Title: Deep Deformable 3D Caricatures with Learned Shape Control
• Authors: Yucheol Jung, Wonjong Jang, Soongjin Kim, Jiaolong Yang, Xin Tong, Seungyong Lee
• Abstract summary: A 3D caricature is an exaggerated 3D depiction of a human face. In this paper, we propose a caricature-based framework for building a deformable surface model. We create variations of 3D surfaces by learning by learning the parameters of a deformable model.
• Score: 25.5491131982863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A 3D caricature is an exaggerated 3D depiction of a human face. The goal of this paper is to model the variations of 3D caricatures in a compact parameter space so that we can provide a useful data-driven toolkit for handling 3D caricature deformations. To achieve the goal, we propose an MLP-based framework for building a deformable surface model, which takes a latent code and produces a 3D surface. In the framework, a SIREN MLP models a function that takes a 3D position on a fixed template surface and returns a 3D displacement vector for the input position. We create variations of 3D surfaces by learning a hypernetwork that takes a latent code and produces the parameters of the MLP. Once learned, our deformable model provides a nice editing space for 3D caricatures, supporting label-based semantic editing and point-handle-based deformation, both of which produce highly exaggerated and natural 3D caricature shapes. We also demonstrate other applications of our deformable model, such as automatic 3D caricature creation.

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