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

Lazova, V., Guzov, V., Olszewski, K., Tulyakov, S., & Pons-Moll, G. (2023). 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: IEEE. doi:10.1109/WACV56688.2023.00432

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@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

Zhang, X., Bhatnagar, B. L., Starke, S., Guzov, V., & Pons-Moll, G. (2022). COUCH: Towards Controllable Human-Chair Interactions. In Computer Vision -- ECCV 2022. Tel Aviv, Israel: Springer. doi:10.1007/978-3-031-20065-6_30

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@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; Cissé, 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

Guzov, V., Sattler, T., & Pons-Moll, G. (2022). Visually Plausible Human-Object Interaction Capture from Wearable Sensors. Retrieved from https://arxiv.org/abs/2205.02830

(arXiv: 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 http://virtualhumans.mpi-inf.mpg.de/hops/

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@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 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 http://virtualhumans.mpi-inf.mpg.de/hops/ }, }

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 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 http://virtualhumans.mpi-inf.mpg.de/hops/ %K Computer Science, Computer Vision and Pattern Recognition, cs.CV

Guzov, V., Mir, A., Sattler,, T., & Pons-Moll, G. (2021). 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): IEEE. doi:10.1109/CVPR46437.2021.00430

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@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