We introduce a new method for non-rigid registration of 3D human shapes. Our
proposed pipeline builds upon a given parametric model of the human, and makes
use of the functional map representation for encoding and inferring shape maps
throughout the registration process. This combination endows our method with
robustness to a large variety of nuisances observed in practical settings,
including non-isometric transformations, downsampling, topological noise, and
occlusions; further, the pipeline can be applied invariably across different
shape representations (e.g. meshes and point clouds), and in the presence of
(even dramatic) missing parts such as those arising in real-world depth sensing
applications. We showcase our method on a selection of challenging tasks,
demonstrating results in line with, or even surpassing, state-of-the-art
methods in the respective areas.

本文介绍了一种针对三维人体形状的非刚性配准方法，该方法基于人体的参数化模型，并利用功能映射表示来在整个配准过程中编码和推断形状映射。该方法具有鲁棒性，可以应对实际环境中出现的各种干扰问题，包括非等距变换、降采样、拓扑噪声和遮挡等，在不同的形状表示下都可以适用，并且即使在出现缺失部分的情况下，也可以应用于实际的深度感测应用中。我们展示了该方法在一系列具有挑战性的任务上的效果，并且证明了该方法在相应领域达到了甚至超越了同类方法的效果。