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- W4298064070 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." @default.
- W4298064070 created "2022-10-01" @default.
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- W4298064070 date "2016-09-05" @default.
- W4298064070 modified "2023-09-29" @default.
- W4298064070 title "Reconstructing Articulated Rigged Models from RGB-D Videos" @default.
- W4298064070 doi "https://doi.org/10.48550/arxiv.1609.01371" @default.
- W4298064070 hasPublicationYear "2016" @default.
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