Browsing by Author "Hwang, Jaepyung"

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    Online Motion Synthesis Framework using a Simple Mass Model based on Predictive Coding
    (ACM, 2019) Hwang, Jaepyung; Ishii, Shin; Oba, Shigeyuki; Batty, Christopher and Huang, Jin
    Hybrid-based character animation utilizing the motion capture data and a simplified physics model allows synthesizing the motion data without losing its naturalness of the original motion. However, using both the physical model and the motion data requires professional insights, experiences, and extra efforts such as preprocessing or off-line optimization. To handle the issue, we propose a new type of motion synthesis framework. The proposed framework combines multiple information sources that generate the reference motion based on the motion capture data and physical constraints based on the physical model. To verify the proposed framework, we define a mass-spring model to represent each skeletal joint of a human character model along with a small amount of motion capture data, a human walking motion.
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    Transition Motion Synthesis for Object Interaction based on Learning Transition Strategies
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Hwang, Jaepyung; Park, Gangrae; Kwon, Taesoo; Ishii, Shin; Hauser, Helwig and Alliez, Pierre
    In this study, we focus on developing a motion synthesis framework that generates a natural transition motion between two different behaviours to interact with a moving object. Specifically, the proposed framework generates the transition motion, bridging from a locomotive behaviour to an object interaction behaviour. And, the transition motion should adapt to the spatio‐temporal variation of the target object in an online manner, so as to naturally connect the behaviours. To solve this issue, we propose a framework that combines a regression model and a transition motion planner. The neural network‐based regression model estimates the reference transition strategy to guide the reference pattern of the transitioning, adapted to the varying situation. The transition motion planner reconstructs the transition motion based on the reference pattern while considering dynamic constraints that avoid the footskate and interaction constraints. The proposed framework is validated to synthesize various transition motions while adapting to the spatio‐temporal variation of the object by using object grasping motion, and athletic motions in soccer.