论文标题
使用电流和varifolds的3D形状比较和分类序列
3D Shape Sequence of Human Comparison and Classification using Current and Varifolds
论文作者
论文摘要
在本文中，我们解决了人类3D形状序列的比较和分类的任务。随着时间的推移，人类运动的非线性动力学和表面参数化的变化使这项任务非常具有挑战性。为了解决这个问题，我们建议将3D形状序列嵌入无限的尺寸空间，即Varifolds的空间，并具有来自给定的正定核的内部产品。更具体地说，我们的方法涉及两个步骤：1）表面表示为varifolds，该表示形式导致指标等效到刚性运动，而不是参数化； 2）3D形状的序列由源自其无限尺寸Hankel矩阵的革兰氏矩阵表示。两个人类的两个3D序列的比较问题被提出为两个革兰甘油矩阵的比较。关于CVSSP3D和DYNA数据集的广泛实验表明，我们的方法在3D人类序列运动检索中与最新的方法具有竞争力。实验代码可在https://github.com/cristal-3dsam/humancomparisonvarifolds上获得。
In this paper we address the task of the comparison and the classification of 3D shape sequences of human. The non-linear dynamics of the human motion and the changing of the surface parametrization over the time make this task very challenging. To tackle this issue, we propose to embed the 3D shape sequences in an infinite dimensional space, the space of varifolds, endowed with an inner product that comes from a given positive definite kernel. More specifically, our approach involves two steps: 1) the surfaces are represented as varifolds, this representation induces metrics equivariant to rigid motions and invariant to parametrization; 2) the sequences of 3D shapes are represented by Gram matrices derived from their infinite dimensional Hankel matrices. The problem of comparison of two 3D sequences of human is formulated as a comparison of two Gram-Hankel matrices. Extensive experiments on CVSSP3D and Dyna datasets show that our method is competitive with state-of-the-art in 3D human sequence motion retrieval. Code for the experiments is available at https://github.com/CRISTAL-3DSAM/HumanComparisonVarifolds.
