In this paper, we present a novel framework for finding the kinematic structure correspondences between two articulated objects in videos via hypergraph matching. In contrast to appearance and graph alignment based matching methods, which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Thus our method allows matching the structure of objects which have similar topologies or motions, or a combination of the two. Our main contributions can be summarised as follows: (i) casting the kinematic structure correspondence problem into a hypergraph matching problem by incorporating multi-order similarities with normalising weights, (ii) introducing a structural topology similarity measure by aggregating topology constrained subgraph isomorphisms, (iii) measuring kinematic correlations between pairwise nodes, and (iv) proposing a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on synthetic and real data, outperforming various other state of the art methods. Our method is not limited to a specific application nor sensor, and can be used as building block in applications such as action recognition, human motion retargeting to robots, and articulated object manipulation amongst others.

中文翻译：

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使用多阶相似性学习运动结构对应


在本文中，我们提出了一种新颖的框架，用于通过超图匹配来查找视频中两个铰接对象之间的运动结构对应关系。与应用于两个相似静态图像之间的基于外观和图形对齐的匹配方法相比，该方法发现视频中异构对象的两个动态运动结构之间的对应关系。因此，我们的方法允许匹配具有相似拓扑或运动或两者组合的对象的结构。我们的主要贡献可以概括如下：（i）通过将多阶相似性与归一化权重结合起来，将运动结构对应问题转化为超图匹配问题，（ii）通过聚合拓扑约束子图同构引入结构拓扑相似性度量，（ iii) 测量成对节点之间的运动学相关性，以及 (iv) 使用黎曼流形上的测地距离提出组合局部运动相似性度量。我们通过对合成数据和真实数据进行的大量实验证明了我们的方法的稳健性和准确性，优于各种其他最先进的方法。我们的方法不限于特定的应用程序或传感器，并且可以用作动作识别、机器人的人体运动重定向和铰接式物体操纵等应用程序的构建块。