Martial Arts Training in Virtual Reality with Full-body Tracking and Physically Simulated Opponents

Takala, Tuukka M.; Hirao, Yutaro; Morikawa, Hiroyuki; Kawai, Takashi

doi:10.1109/vrw50115.2020.00282

Cited by 11 publications

(4 citation statements)

References 2 publications

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“…The predicted data space is nearly infinite. Therefore, GPR can be used to represent the nonlinear input-output mapping, such as martial arts tracking [ 9 ]. The machine learning problem can be expressed as there is a specific dependence between the known variable y and the input x .…”

Section: Machine Learning Methods In Wushu Artsmentioning

confidence: 99%

Visual Information Features and Machine Learning for Wushu Arts Tracking

Zhou

2021

Journal of Healthcare Engineering

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Martial arts tracking is an important research topic in computer vision and artificial intelligence. It has extensive and vital applications in video monitoring, interactive animation and 3D simulation, motion capture, and advanced human-computer interaction. However, due to the change of martial arts’ body posture, clothing variability, and light mixing, the appearance changes significantly. As a result, accurate posture tracking becomes a complicated problem. A solution to this complicated problem is studied in this paper. The proposed solution improves the accuracy of martial arts tracking by the image representation method of martial arts tracking. This method is based on the second-generation strip wave transform and applies it to the video martial arts tracking based on the machine learning method.

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Section: Machine Learning Methods In Wushu Artsmentioning

confidence: 99%

Visual Information Features and Machine Learning for Wushu Arts Tracking

Zhou

2021

Journal of Healthcare Engineering

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“…For instance, [69] uses computer vision techniques and transports the individual to the monitor or screen making use of background subtraction techniques [70]. In turn, [71] also creates the avatar of the person to be introduced into the virtual reality and, thus, be able to practice martial arts in front of virtual enemies that are watched through VR glasses, but in this case, body sensors are used to monitor the movements.…”

Section: Related Workmentioning

confidence: 99%

Toward Modeling Psychomotor Performance in Karate Combats Using Computer Vision Pose Estimation

Echeverria

Santos

2021

Sensors

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Technological advances enable the design of systems that interact more closely with humans in a multitude of previously unsuspected fields. Martial arts are not outside the application of these techniques. From the point of view of the modeling of human movement in relation to the learning of complex motor skills, martial arts are of interest because they are articulated around a system of movements that are predefined, or at least, bounded, and governed by the laws of Physics. Their execution must be learned after continuous practice over time. Literature suggests that artificial intelligence algorithms, such as those used for computer vision, can model the movements performed. Thus, they can be compared with a good execution as well as analyze their temporal evolution during learning. We are exploring the application of this approach to model psychomotor performance in Karate combats (called kumites), which are characterized by the explosiveness of their movements. In addition, modeling psychomotor performance in a kumite requires the modeling of the joint interaction of two participants, while most current research efforts in human movement computing focus on the modeling of movements performed individually. Thus, in this work, we explore how to apply a pose estimation algorithm to extract the features of some predefined movements of Ippon Kihon kumite (a one-step conventional assault) and compare classification metrics with four data mining algorithms, obtaining high values with them.

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“…However, these methods have shortcomings: (1) Digital teaching still relies on "observation," lacking participation for dance students, leading to ineffective learning. (2) The use of high-definition depth cameras with motion sensors is hindered by expensive, non-portable equipment and sensitivity to environmental conditions [6,7], limiting Dunhuang dance promotion.…”

Section: Introductionmentioning

confidence: 99%

Innovative Teaching Method for Dunhuang Dance Incorporating Pose Estimation Fusion

Liu,

Li,

Zhang

et al. 2024

Advances in Intelligent Systems Research

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To overcome the challenges of high costs, venue requirements, and lack of portability in current digital teaching for Dunhuang dance, this study uses Vicon optical motion capture to create a Dunhuang dance motion database and virtual dancers. Using lightweight Convolutional Neural Networks (CNN) for single-view pose estimation in Unity, it captures students' 3D body movements in real-time, driving their virtual avatars. The result is a virtual scene replicating the "one-on-one" teacher-student interaction. This approach enhances user engagement, offering a fresh learning experience and introducing a new teaching paradigm for Dunhuang dance.

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Martial Arts Training in Virtual Reality with Full-body Tracking and Physically Simulated Opponents

Cited by 11 publications

References 2 publications

Visual Information Features and Machine Learning for Wushu Arts Tracking

Visual Information Features and Machine Learning for Wushu Arts Tracking

Toward Modeling Psychomotor Performance in Karate Combats Using Computer Vision Pose Estimation

Innovative Teaching Method for Dunhuang Dance Incorporating Pose Estimation Fusion

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