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pro vyhledávání: '"LAVIOLA JR., JOSEPH J."'
To assess the impact of clutter on egocentric distance perception, we performed a mixed-design study with 60 participants in four different virtual environments (VEs) with three levels of clutter. Additionally, we compared the indoor/outdoor VE chara
Externí odkaz:
http://arxiv.org/abs/2304.08604
Autor:
Caputo, Ariel, Giachetti, Andrea, Soso, Simone, Pintani, Deborah, D'Eusanio, Andrea, Pini, Stefano, Borghi, Guido, Simoni, Alessandro, Vezzani, Roberto, Cucchiara, Rita, Ranieri, Andrea, Giannini, Franca, Lupinetti, Katia, Monti, Marina, Maghoumi, Mehran, LaViola Jr, Joseph J., Le, Minh-Quan, Nguyen, Hai-Dang, Tran, Minh-Triet
Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of application scenarios like Mixed Reality environments, touchless public kiosks, entertainment systems, and more. Recognition of hand gestures can be nowad
Externí odkaz:
http://arxiv.org/abs/2106.10980
Synthetic data generation to improve classification performance (data augmentation) is a well-studied problem. Recently, generative adversarial networks (GAN) have shown superior image data augmentation performance, but their suitability in gesture s
Externí odkaz:
http://arxiv.org/abs/2011.09149
Publikováno v:
2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)
Depth perception of objects can greatly affect a user's experience of an augmented reality (AR) application. Many AR applications require depth matching of real and virtual objects and have the possibility to be influenced by depth cues. Color and lu
Externí odkaz:
http://arxiv.org/abs/2008.05505
Akademický článek
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Autor:
Masnadi, Sina, LaViola Jr., Joseph J., Zhu, Xiaofan, Desingh, Karthik, Jenkins, Odest Chadwicke
In this paper, we present an easy to use sketch-based interface to extract geometries and generate affordance files from 3D point clouds for robot-object interaction tasks. Using our system, even novice users can perform robot task planning by employ
Externí odkaz:
http://arxiv.org/abs/1911.07340
We propose DeepGRU, a novel end-to-end deep network model informed by recent developments in deep learning for gesture and action recognition, that is streamlined and device-agnostic. DeepGRU, which uses only raw skeleton, pose or vector data is quic
Externí odkaz:
http://arxiv.org/abs/1810.12514
Autor:
Caputo, Ariel, Giachetti, Andrea, Soso, Simone, Pintani, Deborah, D’Eusanio, Andrea, Pini, Stefano, Borghi, Guido, Simoni, Alessandro, Vezzani, Roberto, Cucchiara, Rita, Ranieri, Andrea, Giannini, Franca, Lupinetti, Katia, Monti, Marina, Maghoumi, Mehran, LaViola Jr, Joseph J., Le, Minh-Quan, Nguyen, Hai-Dang, Tran, Minh-Triet
Publikováno v:
In Computers & Graphics October 2021 99:201-211
Publikováno v:
Multimodal Technologies & Interaction; Dec2023, Vol. 7 Issue 12, p117, 23p
Akademický článek
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