![]() ![]() Most of these algorithms train the neural network using manually labeled image data and then estimate the human posture, such as joint centers and skeletons, when the user inputs the images or videos to the trained network. Recently, automatic human pose estimation using deep learning techniques have attracted attention amongst computer vision researchers. Therefore, it is desirable to many biomechanics researchers to develop a markerless motion capture that is easy to use for measurement. Markerless measurements without such environmental constraints can facilitate new understanding about human movements ( Mündermann et al., 2006) however, complex information processing technology is required to make an algorithm that recognizes human poses or skeletons from images. For example, measurements cannot be performed in environments wherein wearing markers during the activity is difficult (such as sporting games). However, traditional marker-based approaches have significant environmental constraints. Motion capture systems have been used extensively as a fundamental technology within biomechanics research. In conclusion, this study demonstrates that, if an algorithm that corrects all apparently wrong tracking can be incorporated into the system, OpenPose-based markerless motion capture can be used for human movement science with an accuracy of 30 mm or less. The primary reason for mean absolute errors exceeding 40 mm was that OpenPose failed to track the participant's pose in 2D images owing to failures, such as recognition of an object as a human body segment or replacing one segment with another depending on the image of each frame. Quantitatively, of all the mean absolute errors calculated, approximately 47% were 40 mm. The results demonstrated that, qualitatively, 3D pose estimation using markerless motion capture could correctly reproduce the movements of participants. The differences in corresponding joint positions, estimated from the two different methods throughout the analysis, were presented as a mean absolute error (MAE). Participants performed three motor tasks (walking, countermovement jumping, and ball throwing), and these movements measured using both marker-based optical motion capture and OpenPose-based markerless motion capture. This study aims to develop a 3D markerless motion capture technique, using OpenPose with multiple synchronized video cameras, and examine its accuracy in comparison with optical marker-based motion capture. There is a need within human movement sciences for a markerless motion capture system, which is easy to use and sufficiently accurate to evaluate motor performance. 3Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.2Research Fellow of the Japan Society for the Promotion of Science, Tokyo, Japan.1Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.It was highly realistic, and it was transformed from reality into virtual in real time.Nobuyasu Nakano 1,2 *, Tetsuro Sakura 3, Kazuhiro Ueda 3, Leon Omura 1,2, Arata Kimura 1, Yoichi Iino 1, Senshi Fukashiro 1 and Shinsuke Yoshioka 1 And when her eyes watered up with emotion and fear, the same happened to the virtual character. When she spoke and screamed, the character did, too. ![]() Every time the actress blinked in real life, the character blinked in the animated world onscreen. The actress spoke in real time, and the voice was transformed into an echoed voice of Senua. As the actress’ facial movements, speech, and image was captured in real-time, it showed up on the big screen as Senua, the character in the game. In the demo, actress Melina Juergens played Senua in real time. The game studio showed off an amazing demo today at Epic Games’ briefing at the Game Developers Conference in San Francisco. At the center of it is a woman warrior, Senua, who is battling madness. Ninja Theory is making a highly realistic viking psychological thriller called Hellblade. ![]() Missed the GamesBeat Summit excitement? Don't worry! Tune in now to catch all of the live and virtual sessions here. ![]()
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