Garvita Tiwari (PhD Student)

Garvita Tiwari

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Location
E1 4 - Room 611
Phone
+49 681 9325 2011
Fax
+49 681 9325 2099
Email
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Personal Information

Publications

Bhatnagar, B. L., Tiwari, G., Theobalt, C., & Pons-Moll, G. (n.d.). Multi-Garment Net: Learning to Dress 3D People from Images. In ICCV 2019, International Conference on Computer Vision. Seoul, Korea: IEEE. Retrieved from http://arxiv.org/abs/1908.06903
(arXiv: 1908.06903, Accepted/in press)
Abstract
We present Multi-Garment Network (MGN), a method to predict body shape and clothing, layered on top of the SMPL model from a few frames (1-8) of a video. Several experiments demonstrate that this representation allows higher level of control when compared to single mesh or voxel representations of shape. Our model allows to predict garment geometry, relate it to the body shape, and transfer it to new body shapes and poses. To train MGN, we leverage a digital wardrobe containing 712 digital garments in correspondence, obtained with a novel method to register a set of clothing templates to a dataset of real 3D scans of people in different clothing and poses. Garments from the digital wardrobe, or predicted by MGN, can be used to dress any body shape in arbitrary poses. We will make publicly available the digital wardrobe, the MGN model, and code to dress SMPL with the garments.
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BibTeX
@inproceedings{bhatnagar2019mgn, TITLE = {Multi-Garment Net: {L}earning to Dress {3D} People from Images}, AUTHOR = {Bhatnagar, Bharat Lal and Tiwari, Garvita and Theobalt, Christian and Pons-Moll, Gerard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1908.06903}, EPRINT = {1908.06903}, EPRINTTYPE = {arXiv}, PUBLISHER = {IEEE}, YEAR = {2019}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, ABSTRACT = {We present Multi-Garment Network (MGN), a method to predict body shape and clothing, layered on top of the SMPL model from a few frames (1-8) of a video. Several experiments demonstrate that this representation allows higher level of control when compared to single mesh or voxel representations of shape. Our model allows to predict garment geometry, relate it to the body shape, and transfer it to new body shapes and poses. To train MGN, we leverage a digital wardrobe containing 712 digital garments in correspondence, obtained with a novel method to register a set of clothing templates to a dataset of real 3D scans of people in different clothing and poses. Garments from the digital wardrobe, or predicted by MGN, can be used to dress any body shape in arbitrary poses. We will make publicly available the digital wardrobe, the MGN model, and code to dress SMPL with the garments.}, BOOKTITLE = {ICCV 2019, International Conference on Computer Vision}, ADDRESS = {Seoul, Korea}, }
Endnote
%0 Conference Proceedings %A Bhatnagar, Bharat Lal %A Tiwari, Garvita %A Theobalt, Christian %A Pons-Moll, Gerard %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T Multi-Garment Net: Learning to Dress 3D People from Images : %G eng %U http://hdl.handle.net/21.11116/0000-0004-89E8-C %U http://arxiv.org/abs/1908.06903 %D 2019 %B International Conference on Computer Vision %Z date of event: 2019-10-27 - 2019-11-02 %C Seoul, Korea %X We present Multi-Garment Network (MGN), a method to predict body shape and clothing, layered on top of the SMPL model from a few frames (1-8) of a video. Several experiments demonstrate that this representation allows higher level of control when compared to single mesh or voxel representations of shape. Our model allows to predict garment geometry, relate it to the body shape, and transfer it to new body shapes and poses. To train MGN, we leverage a digital wardrobe containing 712 digital garments in correspondence, obtained with a novel method to register a set of clothing templates to a dataset of real 3D scans of people in different clothing and poses. Garments from the digital wardrobe, or predicted by MGN, can be used to dress any body shape in arbitrary poses. We will make publicly available the digital wardrobe, the MGN model, and code to dress SMPL with the garments. %K Computer Science, Computer Vision and Pattern Recognition, cs.CV %B ICCV 2019 %I IEEE