• Journal of the Korea Computer Graphics Society
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• v.26 no.2
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• pp.11-19
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• 2020

When a character's pose changes, its center of mass(COM) also changes. The change of COM has distinctive patterns corresponding to various motion types like walking, running or sitting. Thus the motion type can be predicted by using COM movement. We propose a motion generator that uses character's center of mass information. This generator can generate various motions without annotated action type labels. Thus dataset for training and running can be generated full-automatically. Our neural network model takes the motion history of the character and its center of mass information as inputs and generates a full-body pose for the current frame, and is trained using simple Convolutional Neural Network(CNN) that performs 1D convolution to deal with time-series motion data.

• Journal of the Korea Computer Graphics Society
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• v.27 no.1
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• pp.9-17
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• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.257-263
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• 2006

Nowadays, 3-D(Three-dimensions) is used in various field such as Games, Movies, Animations etc. Especially the Role of 3-D computer graphic is being generalized by turnover of Screen from '2-dimensional' to '3-dimensional'. There are not enough data about 'Motion Capture' even though it plays main function in 3-dimensional movement. 'Motion Capture' could be also unfamiliar for people. And as a matter of fact, the use of this equipment is limited due to its high cost. Therefore, I studied the outline of Digital Motion Capture system and its application to 3-D game Character Animation. And I checked the movement of 3-D Character after transplanting the Motion Data to the 3-D Character. I acquired Motion Data by Optical Motion Capture Equipment which we possess and transplanted it into the 3-D Character then Implemented it as walking, running and hitting which are the basic motions of Expression.