Technology Transfer Project

Google Faculty Award: Geometry-aware CNNs for Non-Rigid Shape Reconstruction

Type

Technology Transfer Contract

Start Date

17/02/2017

End Date

31/12/2018

Project illustration

Staff

Project Description

This is a project under a Google Faculty Research Award.

This work is a collaboration between PI Francesc Moreno-Noguer, from the Institut de Robòtica i Informàtica Industrial (IRI) at
the Universitat Politècnica de Catalunya-Spanish National Research Council, and co-PI Vincent Lepetit, from University of Bordeaux, France.

Project Publications

Journal Publications

  • A. Agudo and F. Moreno-Noguer. Robust spatio-temporal clustering and reconstruction of multiple deformable bodies. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(4): 971-984, 2019.

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  • A. Agudo and F. Moreno-Noguer. Shape basis interpretation for monocular deformable 3-D reconstruction. IEEE Transactions on Multimedia, 21(4): 821-834, 2019.

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  • A. Agudo and F. Moreno-Noguer. Force-based representation for non-rigid shape and elastic model estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(9): 2137-2150, 2018.

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  • A. Agudo and F. Moreno-Noguer. A scalable, efficient, and accurate solution to non-rigid structure from motion. Computer Vision and Image Understanding, 167: 121-133, 2018.

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Conference Publications

  • A. Agudo, M. Pijoan and F. Moreno-Noguer. Image collection pop-up: 3D reconstruction and clustering of rigid and non-rigid categories, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, Salt Lake City, UT, USA, pp. 2607-2615.

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  • A. Pumarola, A. Agudo, L. Porzi, A. Sanfeliu, V. Lepetit and F. Moreno-Noguer. Geometry-aware network for non-rigid shape prediction from a single view, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, Salt Lake City, UT, USA, pp. 4681-4690.

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  • A. Pumarola, A. Agudo, A. Sanfeliu and F. Moreno-Noguer. Unsupervised person image synthesis in arbitrary poses, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, Salt Lake City, UT, USA, pp. 8620-8628.

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