Eigen Appearance Maps of Dynamic Shapes
We address the problem of building efficient appearance representations of shapes observed from multiple viewpoints and in several movements. Multi-view systems now allow the acquisition of spatio-temporal models of such moving objects. While efficient ge
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bstract. We address the problem of building efficient appearance representations of shapes observed from multiple viewpoints and in several movements. Multi-view systems now allow the acquisition of spatiotemporal models of such moving objects. While efficient geometric representations for these models have been widely studied, appearance information, as provided by the observed images, is mainly considered on a per frame basis, and no global strategy yet addresses the case where several temporal sequences of a shape are available. We propose a per subject representation that builds on PCA to identify the underlying manifold structure of the appearance information relative to a shape. The resulting eigen representation encodes shape appearance variabilities due to viewpoint and motion, with Eigen textures, and due to local inaccuracies in the geometric model, with Eigen warps. In addition to providing compact representations, such decompositions also allow for appearance interpolation and appearance completion. We evaluate their performances over different characters and with respect to their ability to reproduce compelling appearances in a compact way.
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Introduction
The last decade has seen the emergence of 3D dynamic shape models of moving objects, in particular humans, acquired from multiple videos. These spatiotemporal models comprise geometric and appearance information extracted from images, and they allow for subject motions to be recorded and reused. This is of interest for applications that require real 3D contents for analysis, free viewpoint and animation purposes and also for interactive experiences made possible with new virtual reality devices. This ability to now record datasets of subject motions bolsters the need for shape and appearance representations that make optimal use of the massive amount of image information usually produced. While dynamic shape representations have been extensively studied, from temporally coherent representations over a single sequence, to shape spaces that can encode Electronic supplementary material The online version of this chapter (doi:10. 1007/978-3-319-46448-0 14) contains supplementary material, which is available to authorized users. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part I, LNCS 9905, pp. 230–245, 2016. DOI: 10.1007/978-3-319-46448-0 14
Eigen Appearance Maps of Dynamic Shapes
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both pose and subject variabilities over multiple sequences and multiple subjects, appearance representations have received less attention in this context. In this paper, we investigate this issue. Currently, appearance information is still most often estimated and stored once per frame, e.g. a texture map associated to a 3D model [1], and the leap to an efficient temporal appearance representation is still a largely open problem. This is despite the obvious redundancy with which the appearance of subjects is observed, across temporal frames, different viewpoints of the same scene, and often several sequences of the same subject performing d
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