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CageR: Cage-Based Reverse Engineering of Animated 3D Shapes

机译:CageR:基于笼子的3D动画造型逆向工程

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摘要

We present CageR: A novel framework for converting animated 3D shape sequences into compact and stable cagebased representations. Given a raw animated sequence with one-to-one point correspondences together with an initial cage embedding, our algorithm automatically generates smoothly varying cage embeddings which faithfully reconstruct the enclosed object deformation. Our technique is fast, automatic, oblivious to the cage coordinate system, provides controllable error and exploits a GPU implementation. At the core of our method, we introduce a new algebraic algorithm based on maximum volume sub-matrices (maxvol) to speed up and stabilize the deformation inversion. We also present a new spectral regularization algorithm that can apply arbitrary regularization terms on selected subparts of the inversion spectrum. This step allows to enforce a highly localized cage regularization, guaranteeing its smooth variation along the sequence. We demonstrate the speed, accuracy and robustness of our framework on various synthetic and acquired data sets. The benefits of our approach are illustrated in applications such as animation compression and post-editing.
机译:我们提出了CageR:一种将动画3D形状序列转换为紧凑而稳定的基于笼子的表示形式的新颖框架。给定具有一对一点对应关系的原始动画序列以及初始的笼子嵌入,我们的算法会自动生成平滑变化的笼子嵌入,从而忠实地重建封闭的对象变形。我们的技术是快速,自动的,不依赖于保持架坐标系,提供了可控制的错误并利用了GPU实现。作为方法的核心,我们引入了一种基于最大体积子矩阵(maxvol)的新代数算法,以加快并稳定变形反演。我们还提出了一种新的频谱正则化算法,该算法可以将任意正则化项应用于反谱的选定子部分。此步骤可强制执行高度局部的笼子正则化,以确保其沿序列的平滑变化。我们在各种综合和获取的数据集上展示了我们框架的速度,准确性和鲁棒性。我们的方法的好处在动画压缩和后期编辑等应用程序中得到了说明。

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