D2
Computer Vision and Machine Learning

Garvita Tiwari (PhD Student)

MSc Garvita Tiwari

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus
Standort
-
Telefon
+49 681 9325 2000
Fax
+49 681 9325 2099

Personal Information

Publications

Tiwari, G., Sarafianos, N., Tung, T., & Pons-Moll, G. (2021). Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing. Retrieved from https://arxiv.org/abs/2108.08807
(arXiv: 2108.08807)
Abstract
We present Neural Generalized Implicit Functions(Neural-GIF), to animate people in clothing as a function of the body pose. Given a sequence of scans of a subject in various poses, we learn to animate the character for new poses. Existing methods have relied on template-based representations of the human body (or clothing). However such models usually have fixed and limited resolutions, require difficult data pre-processing steps and cannot be used with complex clothing. We draw inspiration from template-based methods, which factorize motion into articulation and non-rigid deformation, but generalize this concept for implicit shape learning to obtain a more flexible model. We learn to map every point in the space to a canonical space, where a learned deformation field is applied to model non-rigid effects, before evaluating the signed distance field. Our formulation allows the learning of complex and non-rigid deformations of clothing and soft tissue, without computing a template registration as it is common with current approaches. Neural-GIF can be trained on raw 3D scans and reconstructs detailed complex surface geometry and deformations. Moreover, the model can generalize to new poses. We evaluate our method on a variety of characters from different public datasets in diverse clothing styles and show significant improvements over baseline methods, quantitatively and qualitatively. We also extend our model to multiple shape setting. To stimulate further research, we will make the model, code and data publicly available at: https://virtualhumans.mpi-inf.mpg.de/neuralgif/
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BibTeX
@online{Tiwari2108.08807, TITLE = {Neural-{GIF}: {N}eural Generalized Implicit Functions for Animating People in Clothing}, AUTHOR = {Tiwari, Garvita and Sarafianos, Nikolaos and Tung, Tony and Pons-Moll, Gerard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2108.08807}, EPRINT = {2108.08807}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {We present Neural Generalized Implicit Functions(Neural-GIF), to animate people in clothing as a function of the body pose. Given a sequence of scans of a subject in various poses, we learn to animate the character for new poses. Existing methods have relied on template-based representations of the human body (or clothing). However such models usually have fixed and limited resolutions, require difficult data pre-processing steps and cannot be used with complex clothing. We draw inspiration from template-based methods, which factorize motion into articulation and non-rigid deformation, but generalize this concept for implicit shape learning to obtain a more flexible model. We learn to map every point in the space to a canonical space, where a learned deformation field is applied to model non-rigid effects, before evaluating the signed distance field. Our formulation allows the learning of complex and non-rigid deformations of clothing and soft tissue, without computing a template registration as it is common with current approaches. Neural-GIF can be trained on raw 3D scans and reconstructs detailed complex surface geometry and deformations. Moreover, the model can generalize to new poses. We evaluate our method on a variety of characters from different public datasets in diverse clothing styles and show significant improvements over baseline methods, quantitatively and qualitatively. We also extend our model to multiple shape setting. To stimulate further research, we will make the model, code and data publicly available at: https://virtualhumans.mpi-inf.mpg.de/neuralgif/}, }
Endnote
%0 Report %A Tiwari, Garvita %A Sarafianos, Nikolaos %A Tung, Tony %A Pons-Moll, Gerard %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations External Organizations Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing : %G eng %U http://hdl.handle.net/21.11116/0000-0009-8C23-2 %U https://arxiv.org/abs/2108.08807 %D 2021 %X We present Neural Generalized Implicit Functions(Neural-GIF), to animate people in clothing as a function of the body pose. Given a sequence of scans of a subject in various poses, we learn to animate the character for new poses. Existing methods have relied on template-based representations of the human body (or clothing). However such models usually have fixed and limited resolutions, require difficult data pre-processing steps and cannot be used with complex clothing. We draw inspiration from template-based methods, which factorize motion into articulation and non-rigid deformation, but generalize this concept for implicit shape learning to obtain a more flexible model. We learn to map every point in the space to a canonical space, where a learned deformation field is applied to model non-rigid effects, before evaluating the signed distance field. Our formulation allows the learning of complex and non-rigid deformations of clothing and soft tissue, without computing a template registration as it is common with current approaches. Neural-GIF can be trained on raw 3D scans and reconstructs detailed complex surface geometry and deformations. Moreover, the model can generalize to new poses. We evaluate our method on a variety of characters from different public datasets in diverse clothing styles and show significant improvements over baseline methods, quantitatively and qualitatively. We also extend our model to multiple shape setting. To stimulate further research, we will make the model, code and data publicly available at: https://virtualhumans.mpi-inf.mpg.de/neuralgif/ %K Computer Science, Computer Vision and Pattern Recognition, cs.CV
Tiwari, G. (2020). Learning Size Sensitive Cloth Model. Universität des Saarlandes, Saarbrücken.
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BibTeX
@mastersthesis{Tiwari_Master2020, TITLE = {Learning Size Sensitive Cloth Model}, AUTHOR = {Tiwari, Garvita}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, }
Endnote
%0 Thesis %A Tiwari, Garvita %Y Pons-Moll, Gerard %A referee: Schiele, Bernt %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T Learning Size Sensitive Cloth Model : %G eng %U http://hdl.handle.net/21.11116/0000-0005-DB6C-C %I Universität des Saarlandes %C Saarbrücken %D 2020 %P 88 p. %V master %9 master
Tiwari, G., Bhatnagar, B. L., Tung, T., & Pons-Moll, G. (2020). SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing. In Computer Vision -- ECCV 2020. Glasgow, UK: Springer. doi:10.1007/978-3-030-58580-8_1
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BibTeX
@inproceedings{tiwari20sizer, TITLE = {SIZER: {A} Dataset and Model for Parsing {3D} Clothing and Learning Size Sensitive {3D} Clothing}, AUTHOR = {Tiwari, Garvita and Bhatnagar, Bharat Lal and Tung, Tony and Pons-Moll, Gerard}, LANGUAGE = {eng}, ISBN = {978-3-030-58579-2}, DOI = {10.1007/978-3-030-58580-8_1}, PUBLISHER = {Springer}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, BOOKTITLE = {Computer Vision -- ECCV 2020}, EDITOR = {Vedaldi, Andrea and Bischof, Horst and Brox, Thomas and Frahm, Jan-Michael}, PAGES = {1--18}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {12348}, ADDRESS = {Glasgow, UK}, }
Endnote
%0 Conference Proceedings %A Tiwari, Garvita %A Bhatnagar, Bharat Lal %A Tung, Tony %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 External Organizations Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing : %G eng %U http://hdl.handle.net/21.11116/0000-0006-E32D-8 %R 10.1007/978-3-030-58580-8_1 %D 2020 %B 16th European Conference on Computer Vision %Z date of event: 2020-08-23 - 2020-08-28 %C Glasgow, UK %B Computer Vision -- ECCV 2020 %E Vedaldi, Andrea; Bischof, Horst; Brox, Thomas; Frahm, Jan-Michael %P 1 - 18 %I Springer %@ 978-3-030-58579-2 %B Lecture Notes in Computer Science %N 12348
Bhatnagar, B. L., Tiwari, G., Theobalt, C., & Pons-Moll, G. (2019). Multi-Garment Net: Learning to Dress 3D People from Images. In International Conference on Computer Vision (ICCV 2019). Seoul, Korea: IEEE. doi:10.1109/ICCV.2019.00552
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BibTeX
@inproceedings{bhatnagar_ICCV2019, TITLE = {Multi-Garment {N}et: {L}earning to Dress {3D} People from Images}, AUTHOR = {Bhatnagar, Bharat Lal and Tiwari, Garvita and Theobalt, Christian and Pons-Moll, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-7281-4803-8}, DOI = {10.1109/ICCV.2019.00552}, PUBLISHER = {IEEE}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {International Conference on Computer Vision (ICCV 2019)}, PAGES = {5419--5429}, 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 %R 10.1109/ICCV.2019.00552 %D 2019 %B International Conference on Computer Vision %Z date of event: 2019-10-27 - 2019-11-02 %C Seoul, Korea %B International Conference on Computer Vision %P 5419 - 5429 %I IEEE %@ 978-1-7281-4803-8