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Data-Driven Human Modeling by Sparse Representation
Computer-Aided Design ( IF 4.3 ) Pub Date : 2020-07-15 , DOI: 10.1016/j.cad.2020.102913
Yiu-Bun Wu , Bin Liu , Xiuping Liu , Charlie C.L. Wang

Data-driven methods for modeling the realistic shapeof 3D human bodies need to access datasets that contain a large amount of 3D human models. We develop a method based on sparse representation in this paper to represent 3D human models as signals of patches. Unlike the general mesh compression approaches, all mesh models used in a data-driven human modeling framework have the same mesh connectivity. By using this property, we segment a human model into patches containing the same number of vertices. L0-learning algorithm is selected to train an overcomplete dictionary matrix, which in turn introduces sparse representation of the dataset. Patch signals of individual human models can then be extracted by using the dictionary matrix. With the ease of balance control between sparsity and accuracy that is featured by the chosen learning algorithm, a representation with high compression ratio and low shape-approximation error can be determined. The results have been compared with the widely used statistic representation based on principal component analysis (PCA) to verify the effectiveness of our approach. Moreover, the method for using sparse representation in the regression-based statistical modeling of 3D human models has been presented at the end of the paper.



中文翻译:

稀疏表示法的数据驱动人体建模

用于建模3D人体真实形状的数据驱动方法需要访问包含大量3D人体模型的数据集。在本文中,我们开发了一种基于稀疏表示的方法来将3D人体模型表示为补丁信号。与一般的网格压缩方法不同,在数据驱动的人类建模框架中使用的所有网格模型都具有相同的网格连接性。通过使用此属性,我们将人体模型分割为包含相同数量顶点的面片。大号0选择学习算法来训练过完备的字典矩阵,这又引入了数据集的稀疏表示。然后可以使用字典矩阵提取各个人体模型的补丁信号。通过所选择的学习算法所具有的易于控制的稀疏性和准确性之间的平衡,可以确定具有高压缩比和低形状近似误差的表示。将结果与基于主成分分析(PCA)的广泛使用的统计表示形式进行了比较,以验证我们方法的有效性。此外,在本文结尾处提出了在基于回归的3D人体模型统计模型中使用稀疏表示的方法。

更新日期:2020-07-15
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