In this paper, we address the problem of building dense correspondences
between human images under arbitrary camera viewpoints and body poses. Prior
art either assumes small motion between frames or relies on local descriptors,
which cannot handle large motion or visually ambiguous body parts, e.g., left
vs. right hand. In contrast, we propose a deep learning framework that maps
each pixel to a feature space, where the feature distances reflect the geodesic
distances among pixels as if they were projected onto the surface of a 3D human
scan. To this end, we introduce novel loss functions to push features apart
according to their geodesic distances on the surface. Without any semantic
annotation, the proposed embeddings automatically learn to differentiate
visually similar parts and align different subjects into an unified feature
space. Extensive experiments show that the learned embeddings can produce
accurate correspondences between images with remarkable generalization
capabilities on both intra and inter subjects.

本文提出了一种利用深度学习框架将每个像素映射到特征空间的方法来构建人类图像之间的密集对应关系，并提出了新的损失函数来推动特征根据它们在表面上的测地距离分开，实现视觉上相似的部分差异化以及通过统一的特征空间对不同的主题进行对齐，实验结果表明，该特征空间可以对图像之间产生准确的对应关系，并具有明显的推广能力。