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SCAPE: shape completion and animation of people

Open access
Anguelov, Dragomir, Srinivasan, Praveen, Koller, Daphne, Thrun, Sebastian, Rodgers, Jim, Davis, James·2005·ACM SIGGRAPH 2005 Papers

Abstract

We introduce the SCAPE method (Shape Completion and Animation for PEople)---a data-driven method for building a human shape model that spans variation in both subject shape and pose. The method is based on a representation that incorporates both articulated and non-rigid deformations. We learn a pose deformation model that derives the non-rigid surface deformation as a function of the pose of the articulated skeleton. We also learn a separate model of variation based on body shape. Our two models can be combined to produce 3D surface models with realistic muscle deformation for different people in different poses, when neither appear in the training set. We show how the model can be used for shape completion --- generating a complete surface mesh given a limited set of markers specifying the target shape. We present applications of shape completion to partial view completion and motion capture animation. In particular, our method is capable of constructing a high-quality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan and a marker motion capture sequence of the person.

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Metadata

Authors
Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J.
Venue
ACM SIGGRAPH 2005 Papers
Publisher
ACM
Published
2005-07
Pages
408-416
OpenAlex
W4236667477
Cited by
410
References
0
FWCI
8.79
Type
conference paper
Language
en
Open access
bronze

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CoChat. (2026). Citation check: SCAPE: shape completion and animation of people. https://app.cochat.ai/papers/10.1145/1186822.1073207
The paper
Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005). SCAPE: shape completion and animation of people. ACM SIGGRAPH 2005 Papers. https://doi.org/10.1145/1186822.1073207

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.