Understanding how an animal can deform and articulate is essential for a
realistic modification of its 3D model. In this paper, we show that such
information can be learned from user-clicked 2D images and a template 3D model
of the target animal. We present a volumetric deformation framework that
produces a set of new 3D models by deforming a template 3D model according to a
set of user-clicked images. Our framework is based on a novel locally-bounded
deformation energy, where every local region has its own stiffness value that
bounds how much distortion is allowed at that location. We jointly learn the
local stiffness bounds as we deform the template 3D mesh to match each
user-clicked image. We show that this seemingly complex task can be solved as a
sequence of convex optimization problems. We demonstrate the effectiveness of
our approach on cats and horses, which are highly deformable and articulated
animals. Our framework produces new 3D models of animals that are significantly
more plausible than methods without learned stiffness.

本文提出了一种基于用户标记图片和动物模板的体积变形框架，通过学习每个局部区域的硬度值，动态调整模板三维网格来实现动物变形与模拟，其优于未学习硬度值的方法，适用于高度可形变和关节灵活的动物模型。