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안혜민

Ahn, Hyemin
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dc.citation.conferencePlace UK -
dc.citation.conferencePlace London, United Kingdom -
dc.citation.title IEEE International Conference on Robotics and Automation -
dc.contributor.author Ahn, Hyemin -
dc.contributor.author Mascaro, Esteve Valls -
dc.contributor.author Lee, Dongheui -
dc.date.accessioned 2024-01-26T12:35:09Z -
dc.date.available 2024-01-26T12:35:09Z -
dc.date.created 2023-12-01 -
dc.date.issued 2023-05-29 -
dc.description.abstract 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. -
dc.identifier.bibliographicCitation IEEE International Conference on Robotics and Automation -
dc.identifier.doi 10.1109/icra48891.2023.10160722 -
dc.identifier.scopusid 2-s2.0-85168667582 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/72412 -
dc.language 영어 -
dc.publisher IEEE -
dc.title Can We Use Diffusion Probabilistic Models for 3D Motion Prediction? -
dc.type Conference Paper -
dc.date.conferenceDate 2023-05-29 -

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