Abstract

We present a novel framework for converting animated 3D shape sequences into compact and stable cage-based 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 sub-parts 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.

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Bibtex

@article{TTB:CageR:CGF,
  author = {Jean-Marc Thiery and Julien Tierny and Tamy Boubekeur},
  title = {CageR: Cage-based Reverse Engineering of Animated 3D Shapes},
  journal ={Computer Graphics Forum},
  year = {2012},
  volume = {31},
  number = {8},
  pages = {2303--2316},
}

Related publications

CageR: From 3D Performance Capture to Cage-based Representation
Jean-Marc Thiery, Julien Tierny and Tamy Boubekeur
SIGGRAPH 2012 - Talk Program

Related projects

• Research on 3D Shape Approximation

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