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Lie Bodies: A Manifold Representation of 3D Human Shape

机译:谎言实体:3D人类形状的流形表示

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Three-dimensional object shape is commonly represented in terms of deformations of a triangular mesh from an exemplar shape. Existing models, however, are based on a Euclidean representation of shape deformations. In contrast, we argue that shape has a manifold structure: For example, summing the shape deformations for two people does not necessarily yield a deformation corresponding to a valid human shape, nor does the Euclidean difference of these two deformations provide a meaningful measure of shape dissimilarity. Consequently, we define a novel manifold for shape representation, with emphasis on body shapes, using a new Lie group of deformations. This has several advantages. First we define triangle deformations exactly, removing non-physical deformations and redundant degrees of freedom common to previous methods. Second, the Riemannian structure of Lie Bodies enables a more meaningful definition of body shape similarity by measuring distance between bodies on the manifold of body shape deformations. Third, the group structure allows the valid composition of deformations. This is important for models that factor body shape deformations into multiple causes or represent shape as a linear combination of basis shapes. Finally, body shape variation is modeled using statistics on manifolds. Instead of modeling Euclidean shape variation with Principal Component Analysis we capture shape variation on the manifold using Principal Geodesic Analysis. Our experiments show consistent visual and quantitative advantages of Lie Bodies over traditional Euclidean models of shape deformation and our representation can be easily incorporated into existing methods.
机译:三维物体形状通常以三角形网格从示例形状的变形来表示。但是,现有模型基于形状变形的欧几里得表示。相反,我们认为形状具有流形结构:例如,将两个人的形状变形相加并不一定会产生与有效人形相对应的变形,这两个变形的欧几里德差也不能提供有意义的形状度量不相似。因此,我们使用新的Lie变形组定义了一种新颖的歧管,以形状表示为重点,着重于身体形状。这具有几个优点。首先,我们精确地定义三角形变形,消除非物理变形和先前方法常见的冗余自由度。其次,Lie Bodies的Riemannian结构通过测量体形变形的流形上的体间距离,可以更有意义地定义体形相似性。第三,组结构允许变形的有效组合。这对于将身体形状变形分解为多种原因或将形状表示为基本形状的线性组合的模型非常重要。最后,使用歧管上的统计数据对身体形状的变化进行建模。代替使用主成分分析为欧几里得形状变化建模,我们使用主测地线分析捕获流形上的形状变化。我们的实验表明,与传统的形状变形欧几里德模型相比,Lie Bodies具有一致的视觉和定量优势,我们的表示可以轻松地并入现有方法中。

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