Ahn, Hyemin und Mascaro, Esteve Valls und Lee, Dongheui (2023) Can We Use Diffusion Probabilistic Models for 3D Motion Prediction? In: 2023 IEEE International Conference on Robotics and Automation, ICRA 2023, Seiten 9837-9843. IEEE. 2023 IEEE International Conference on Robotics and Automation, 2023-05-29 - 2023-06-02, London, UK. doi: 10.1109/ICRA48891.2023.10160722. ISBN 979-835032365-8. ISSN 1050-4729.
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Offizielle URL: https://ieeexplore.ieee.org/document/10160722
Kurzfassung
After many researchers observed fruitfulness from the recent diffusion probabilistic model, its effectiveness in image generation is actively studied these days. In this paper, our objective is to evaluate the potential of diffusion probabilistic models for 3D human motion-related tasks. To this end, this pa-per presents a study of employing diffusion probabilistic models to predict future 3D human motion(s) from the previously observed motion. Based on the Human 3.6M and HumanEva-I datasets, our results show that diffusion probabilistic models are competitive for both single (deterministic) and multiple (stochastic) 3D motion prediction tasks, after finishing a single training process. In addition, we find out that diffusion probabilistic models can offer an attractive compromise, since they can strike the right balance between the likelihood and diversity of the predicted future motions. Our code is publicly available on the project website: https://sites.google.com/view/diffusion-motion-prediction.
elib-URL des Eintrags: | https://elib.dlr.de/197486/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||
Titel: | Can We Use Diffusion Probabilistic Models for 3D Motion Prediction? | ||||||||||||||||
Autoren: |
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Datum: | 4 Juli 2023 | ||||||||||||||||
Erschienen in: | 2023 IEEE International Conference on Robotics and Automation, ICRA 2023 | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Nein | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||
DOI: | 10.1109/ICRA48891.2023.10160722 | ||||||||||||||||
Seitenbereich: | Seiten 9837-9843 | ||||||||||||||||
Verlag: | IEEE | ||||||||||||||||
ISSN: | 1050-4729 | ||||||||||||||||
ISBN: | 979-835032365-8 | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | Diffusion Probabilistic Models | ||||||||||||||||
Veranstaltungstitel: | 2023 IEEE International Conference on Robotics and Automation | ||||||||||||||||
Veranstaltungsort: | London, UK | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsbeginn: | 29 Mai 2023 | ||||||||||||||||
Veranstaltungsende: | 2 Juni 2023 | ||||||||||||||||
Veranstalter : | IEEE | ||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||
HGF - Programmthema: | Robotik | ||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||
DLR - Forschungsgebiet: | R RO - Robotik | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Basistechnologien [RO] | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) | ||||||||||||||||
Hinterlegt von: | Strobl, Dr. Klaus H. | ||||||||||||||||
Hinterlegt am: | 22 Sep 2023 12:57 | ||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:57 |
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