Vladimir Guzov (PhD Student)

MSc Vladimir Guzov

Address: Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus
Location: -
Phone: +49 681 9325 2000
Fax: +49 681 9325 2099
E-mail: Get email via email

Personal Information

Publications

2023

Conference paper

V. Lazova, V. Guzov, K. Olszewski, S. Tulyakov, and G. Pons-Moll

“Control-NeRF: Editable Feature Volumes for Scene Rendering and Manipulation,” in 2023 IEEE Winter Conference on Applications of Computer Vision (WACV 2023), Waikoloa Village, HI, USA, 2023.

@inproceedings{LazovaWACV23,
TITLE = {Control-{NeRF}: Editable Feature Volumes for Scene Rendering and Manipulation},
AUTHOR = {Lazova, Verica and Guzov, Vladimir and Olszewski, Kyle and Tulyakov, Sergey and Pons-Moll, Gerard},
LANGUAGE = {eng},
ISBN = {978-1-6654-9346-8},
DOI = {10.1109/WACV56688.2023.00432},
PUBLISHER = {IEEE},
YEAR = {2023},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {2023 IEEE Winter Conference on Applications of Computer Vision (WACV 2023)},
PAGES = {4329--4339},
ADDRESS = {Waikoloa Village, HI, USA},
}

Endnote

%0 Conference Proceedings
%A Lazova, Verica
%A Guzov, Vladimir
%A Olszewski, Kyle
%A Tulyakov, Sergey
%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
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Control-NeRF: Editable Feature Volumes for Scene Rendering and
  Manipulation : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000C-2A9F-3
%R 10.1109/WACV56688.2023.00432
%D 2023
%B IEEE Winter Conference on Applications of Computer Vision
%Z date of event: 2023-01-03 - 2023-01-07
%C Waikoloa Village, HI, USA
%B 2023 IEEE Winter Conference on Applications of Computer Vision
%P 4329 - 4339
%I IEEE
%@ 978-1-6654-9346-8

2022

Conference paper

X. Zhang, B. L. Bhatnagar, S. Starke, V. Guzov, and G. Pons-Moll

“COUCH: Towards Controllable Human-Chair Interactions,” in Computer Vision -- ECCV 2022, Tel Aviv, Israel, 2022.

@inproceedings{Zhang_ECCV2022,
TITLE = {{COUCH}: {T}owards Controllable Human-Chair Interactions},
AUTHOR = {Zhang, Xiaohan and Bhatnagar, Bharat Lal and Starke, Sebastian and Guzov, Vladimir and Pons-Moll, Gerard},
LANGUAGE = {eng},
ISBN = {978-3-031-20064-9},
DOI = {10.1007/978-3-031-20065-6_30},
PUBLISHER = {Springer},
YEAR = {2022},
MARGINALMARK = {$\bullet$},
DATE = {2022},
BOOKTITLE = {Computer Vision -- ECCV 2022},
EDITOR = {Avidan, Shai and Brostow, Gabriel and Ciss{\'e}, Moustapha and Farinella, Giovanni and Hassner, Tal},
PAGES = {518--535},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {13665},
ADDRESS = {Tel Aviv, Israel},
}

Endnote

%0 Conference Proceedings
%A Zhang, Xiaohan
%A Bhatnagar, Bharat Lal
%A Starke, Sebastian
%A Guzov, Vladimir
%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
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T COUCH: Towards Controllable Human-Chair Interactions : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000C-2A92-0
%R 10.1007/978-3-031-20065-6_30
%D 2022
%B 17th European Conference on Computer Vision
%Z date of event: 2022-10-23 - 2022-10-27
%C Tel Aviv, Israel
%B Computer Vision -- ECCV 2022
%E Avidan, Shai; Brostow, Gabriel; Ciss&#233;, Moustapha; Farinella, Giovanni; Hassner, Tal
%P 518 - 535
%I Springer
%@ 978-3-031-20064-9
%B Lecture Notes in Computer Science
%N 13665
%U https://rdcu.be/c26Qf

Paper

V. Guzov, T. Sattler, and G. Pons-Moll

“Visually Plausible Human-Object Interaction Capture from Wearable Sensors,” 2022. [Online]. Available: https://arxiv.org/abs/2205.02830.

Abstract

In everyday lives, humans naturally modify the surrounding environment
through interactions, e.g., moving a chair to sit on it. To reproduce such
interactions in virtual spaces (e.g., metaverse), we need to be able to capture
and model them, including changes in the scene geometry, ideally from
ego-centric input alone (head camera and body-worn inertial sensors). This is
an extremely hard problem, especially since the object/scene might not be
visible from the head camera (e.g., a human not looking at a chair while
sitting down, or not looking at the door handle while opening a door). In this
paper, we present HOPS, the first method to capture interactions such as
dragging objects and opening doors from ego-centric data alone. Central to our
method is reasoning about human-object interactions, allowing to track objects
even when they are not visible from the head camera. HOPS localizes and
registers both the human and the dynamic object in a pre-scanned static scene.
HOPS is an important first step towards advanced AR/VR applications based on
immersive virtual universes, and can provide human-centric training data to
teach machines to interact with their surroundings. The supplementary video,
data, and code will be available on our project page at
virtualhumans.mpi-inf.mpg.de/hops/

BibTeX

@online{Guzov2205.02830,
TITLE = {Visually Plausible Human-Object Interaction Capture from Wearable Sensors},
AUTHOR = {Guzov, Vladimir and Sattler, Torsten and Pons-Moll, Gerard},
LANGUAGE = {eng},
URL = {https://arxiv.org/abs/2205.02830},
EPRINT = {2205.02830},
EPRINTTYPE = {arXiv},
YEAR = {2022},
MARGINALMARK = {$\bullet$},
ABSTRACT = {In everyday lives, humans naturally modify the surrounding environment<br>through interactions, e.g., moving a chair to sit on it. To reproduce such<br>interactions in virtual spaces (e.g., metaverse), we need to be able to capture<br>and model them, including changes in the scene geometry, ideally from<br>ego-centric input alone (head camera and body-worn inertial sensors). This is<br>an extremely hard problem, especially since the object/scene might not be<br>visible from the head camera (e.g., a human not looking at a chair while<br>sitting down, or not looking at the door handle while opening a door). In this<br>paper, we present HOPS, the first method to capture interactions such as<br>dragging objects and opening doors from ego-centric data alone. Central to our<br>method is reasoning about human-object interactions, allowing to track objects<br>even when they are not visible from the head camera. HOPS localizes and<br>registers both the human and the dynamic object in a pre-scanned static scene.<br>HOPS is an important first step towards advanced AR/VR applications based on<br>immersive virtual universes, and can provide human-centric training data to<br>teach machines to interact with their surroundings. The supplementary video,<br>data, and code will be available on our project page at<br>http://virtualhumans.mpi-inf.mpg.de/hops/<br>},
}

Endnote

%0 Report
%A Guzov, Vladimir
%A Sattler, Torsten
%A Pons-Moll, Gerard
%+ 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 Visually Plausible Human-Object Interaction Capture from Wearable
  Sensors : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000C-2A9C-6
%U https://arxiv.org/abs/2205.02830
%D 2022
%X   In everyday lives, humans naturally modify the surrounding environment<br>through interactions, e.g., moving a chair to sit on it. To reproduce such<br>interactions in virtual spaces (e.g., metaverse), we need to be able to capture<br>and model them, including changes in the scene geometry, ideally from<br>ego-centric input alone (head camera and body-worn inertial sensors). This is<br>an extremely hard problem, especially since the object/scene might not be<br>visible from the head camera (e.g., a human not looking at a chair while<br>sitting down, or not looking at the door handle while opening a door). In this<br>paper, we present HOPS, the first method to capture interactions such as<br>dragging objects and opening doors from ego-centric data alone. Central to our<br>method is reasoning about human-object interactions, allowing to track objects<br>even when they are not visible from the head camera. HOPS localizes and<br>registers both the human and the dynamic object in a pre-scanned static scene.<br>HOPS is an important first step towards advanced AR/VR applications based on<br>immersive virtual universes, and can provide human-centric training data to<br>teach machines to interact with their surroundings. The supplementary video,<br>data, and code will be available on our project page at<br>http://virtualhumans.mpi-inf.mpg.de/hops/<br>
%K Computer Science, Computer Vision and Pattern Recognition, cs.CV

2021

Conference paper

V. Guzov, A. Mir, T. Sattler,, and G. Pons-Moll

“Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), Nashville, TN, US (Virtual), 2021.

@inproceedings{Guzov_CVPR21,
TITLE = {Human {POSEitioning} System ({HPS}): {3D} Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors},
AUTHOR = {Guzov, Vladimir and Mir, Aymen and Sattler,, Torsten and Pons-Moll, Gerard},
LANGUAGE = {eng},
ISBN = {978-1-6654-4509-2},
DOI = {10.1109/CVPR46437.2021.00430},
PUBLISHER = {IEEE},
YEAR = {2021},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)},
PAGES = {4318--4329},
ADDRESS = {Nashville, TN, US (Virtual)},
}

Endnote

%0 Conference Proceedings
%A Guzov, Vladimir
%A Mir, Aymen
%A Sattler,, Torsten
%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 Human POSEitioning System (HPS): 3D Human Pose Estimation and
Self-localization in Large Scenes from Body-Mounted Sensors : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0009-8AF2-A
%R 10.1109/CVPR46437.2021.00430
%D 2021
%B 34th IEEE Conference on Computer Vision and Pattern Recognition
%Z date of event: 2021-06-19 - 2021-06-25
%C Nashville, TN, US (Virtual)
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition
%P 4318 - 4329
%I IEEE
%@ 978-1-6654-4509-2