论文标题
NILB：神经逆线性混合皮肤
NiLBS: Neural Inverse Linear Blend Skinning
论文作者
论文摘要
在这份技术报告中，我们研究了铰接对象（例如人体）的有效表示，这是计算机视觉和图形中的重要问题。为了变形铰接式几何形状，现有的方法将对象表示为网络，并使用“剥皮”技术将它们变形。皮肤操作允许使用少量控制参数实现多种变形。本文介绍了一种通过传统的皮肤技术通过姿势参数参数的神经网络逆转变形的方法。倒转这些变形的能力允许在静止姿势预先计算值（例如，距离函数，签名距离函数，占用率），然后在字符变形时有效查询。我们对未来工作的方法进行经验评估。
In this technical report, we investigate efficient representations of articulated objects (e.g. human bodies), which is an important problem in computer vision and graphics. To deform articulated geometry, existing approaches represent objects as meshes and deform them using "skinning" techniques. The skinning operation allows a wide range of deformations to be achieved with a small number of control parameters. This paper introduces a method to invert the deformations undergone via traditional skinning techniques via a neural network parameterized by pose. The ability to invert these deformations allows values (e.g., distance function, signed distance function, occupancy) to be pre-computed at rest pose, and then efficiently queried when the character is deformed. We leave empirical evaluation of our approach to future work.
