论文标题
卡尔维斯：胸部，腰部和骨盆圆周来自3D人体网眼，作为深度学习的地面真理
CALVIS: chest, waist and pelvis circumference from 3D human body meshes as ground truth for deep learning
论文作者
论文摘要
在本文中，我们介绍了Calvis，这是一种计算$ \ textbf {c} $ hest，w $ \ textbf {a} $ ist和pe $ \ textbf {lvis} $ curver的方法。我们的动机是将这些数据用作培训卷积神经网络（CNN）的基础真理。先前的工作使用了大规模的凯撒数据集或从一个人或人类3D身体网格中确定了这些人类测量$ \ textit {手动} $。不幸的是，获取这些数据是一项成本和耗时的努力。相反，我们的方法可以自动在3D网格上使用。我们合成了八个人体网格，并应用Calvis来计算胸部，腰部和骨盆圆周。我们定性地评估结果，并观察到测量确实可以用来估计一个人的形状。然后，我们通过与卡尔维斯（Calvis）培训小型有线电视新闻网（CNN）来提高方法的合理性。通过我们的数据训练网络后，我们达到了竞争性验证错误。此外，我们将公开实施Calvis实施以推进该领域。
In this paper we present CALVIS, a method to calculate $\textbf{C}$hest, w$\textbf{A}$ist and pe$\textbf{LVIS}$ circumference from 3D human body meshes. Our motivation is to use this data as ground truth for training convolutional neural networks (CNN). Previous work had used the large scale CAESAR dataset or determined these anthropometrical measurements $\textit{manually}$ from a person or human 3D body meshes. Unfortunately, acquiring these data is a cost and time consuming endeavor. In contrast, our method can be used on 3D meshes automatically. We synthesize eight human body meshes and apply CALVIS to calculate chest, waist and pelvis circumference. We evaluate the results qualitatively and observe that the measurements can indeed be used to estimate the shape of a person. We then asses the plausibility of our approach by generating ground truth with CALVIS to train a small CNN. After having trained the network with our data, we achieve competitive validation error. Furthermore, we make the implementation of CALVIS publicly available to advance the field.
