Perceiving Systems, Computer Vision

Reconstructing Articulated Rigged Models from RGB-D Videos

2016

Conference Paper

ps

Although commercial and open-source software exist to reconstruct a static object from a sequence recorded with an RGB-D sensor, there is a lack of tools that build rigged models of articulated objects that deform realistically and can be used for tracking or animation. In this work, we fill this gap and propose a method that creates a fully rigged model of an articulated object from depth data of a single sensor. To this end, we combine deformable mesh tracking, motion segmentation based on spectral clustering and skeletonization based on mean curvature flow. The fully rigged model then consists of a watertight mesh, embedded skeleton, and skinning weights.

Author(s): Dimitrios Tzionas and Juergen Gall
Book Title: European Conference on Computer Vision Workshops 2016 (ECCVW’16) - Workshop on Recovering 6D Object Pose (R6D’16)
Pages: 620--633
Year: 2016
Publisher: Springer International Publishing
Department(s): Perceiving Systems
Research Project(s): Hands-Object Interaction
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Workshop
DOI: 10.1007/978-3-319-49409-8_53
URL: http://files.is.tue.mpg.de/dtzionas/Skeleton-Reconstruction
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BibTex 

@inproceedings{Tzionas:ECCVw:2016,
  title = {Reconstructing Articulated Rigged Models from RGB-D Videos},
  author = {Tzionas, Dimitrios and Gall, Juergen},
  booktitle = {European Conference on Computer Vision Workshops 2016 (ECCVW'16) - Workshop on Recovering 6D Object Pose (R6D'16)},
  pages = {620--633},
  publisher = {Springer International Publishing},
  year = {2016},
  doi = {10.1007/978-3-319-49409-8_53},
  url = {http://files.is.tue.mpg.de/dtzionas/Skeleton-Reconstruction}
}