• Journal of Korea Game Society
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• v.21 no.5
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• pp.41-48
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• 2021

This paper introduces a mobile augmented visualization technology that augments a three-dimensional virtual human body on a mannequin model using two pose(position and rotation) tracking sensors. The conventional camera tracking technology used for augmented visualization has the disadvantage of failing to calculate the camera pose when the camera shakes or moves quickly because it uses the camera image, but using a pose tracking sensor can overcome this disadvantage. Also, even if the position of the mannequin is changed or rotated, augmented visualization is possible using the data of the pose tracking sensor attached to the mannequin, and above all there is no load for camera tracking.

• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.11-20
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• 2023

In this paper, we propose a novel deep learning-based motion reconstruction approach that facilitates the generation of full-body motions, including finger motions, while also enabling the online adjustment of motion generation delays. The proposed method combines the Vive Tracker with a deep learning method to achieve more accurate motion reconstruction while effectively mitigating foot skating issues through the use of an Inverse Kinematics (IK) solver. The proposed method utilizes a trained AutoEncoder to reconstruct character body motions using tracker data in real-time while offering the flexibility to adjust motion generation delays as needed. To generate hand motions suitable for the reconstructed body motion, we employ a Fully Connected Network (FCN). By combining the reconstructed body motion from the AutoEncoder with the hand motions generated by the FCN, we can generate full-body motions of characters that include hand movements. In order to alleviate foot skating issues in motions generated by deep learning-based methods, we use an IK solver. By setting the trackers located near the character's feet as end-effectors for the IK solver, our method precisely controls and corrects the character's foot movements, thereby enhancing the overall accuracy of the generated motions. Through experiments, we validate the accuracy of motion generation in the proposed deep learning-based motion reconstruction scheme, as well as the ability to adjust latency based on user input. Additionally, we assess the correction performance by comparing motions with the IK solver applied to those without it, focusing particularly on how it addresses the foot skating issue in the generated full-body motions.