Building Statistical Shape Spaces for 3D Human Modeling

Leonid Pishchulin 1 Stefanie Wuhrer 2 Thomas Helten 1 Christian Theobalt 1 Bernt Schiele 1
2 MORPHEO - Capture and Analysis of Shapes in Motion
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Statistical models of 3D human shape and pose learned from scan databases have developed into valuable tools to solve a variety of vision and graphics problems. Unfortunately, most publicly available models are of limited expressiveness as they were learned on very small databases that hardly reflect the true variety in human body shapes. In this paper, we contribute by rebuilding a widely used statistical body representation from the largest commercially available scan database, and making the resulting model available to the community (visit ). As preprocessing several thousand scans for learning the model is a challenge in itself, we contribute by developing robust best practice solutions for scan alignment that quantitatively lead to the best learned models. We make implementations of these preprocessing steps also publicly available. We extensively evaluate the improved accuracy and generality of our new model, and show its improved performance for human body reconstruction from sparse input data.
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Article dans une revue
Pattern Recognition, Elsevier, 2017, 67, pp.276-286. 〈10.1016/j.patcog.2017.02.018〉
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Soumis le : vendredi 3 mars 2017 - 10:38:14
Dernière modification le : jeudi 20 septembre 2018 - 07:54:02


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Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt, Bernt Schiele. Building Statistical Shape Spaces for 3D Human Modeling. Pattern Recognition, Elsevier, 2017, 67, pp.276-286. 〈10.1016/j.patcog.2017.02.018〉. 〈hal-01136221v2〉



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