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Reconstructing Articulated Rigged Models from RGB-D Videos
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.
@inproceedings{Tzionas:ECCVw:2016, title = {Reconstructing Articulated Rigged Models from RGB-D Videos}, booktitle = {European Conference on Computer Vision Workshops 2016 (ECCVW'16) - Workshop on Recovering 6D Object Pose (R6D'16)}, abstract = {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.}, pages = {620--633}, publisher = {Springer International Publishing}, year = {2016}, slug = {tzionas-eccvw-2015}, author = {Tzionas, Dimitrios and Gall, Juergen}, url = {http://files.is.tue.mpg.de/dtzionas/Skeleton-Reconstruction} }