Browsing by Author "Lee, Jehee"

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Real-Time Physics-Based 3D Biped Character Animation Using an Inverted Pendulum Model
(2010-03-01)
Tsai, Yao-Yang
;
Lin, Wen-Chieh
;
Cheng, Kuangyou B.
;
Lee, Jehee
;
Lee, Tong-Yee
We present a physics-based approach to generate 3D biped character animation that can react to dynamical environments in real time. Our approach utilizes an inverted pendulum model to online adjust the desired motion trajectory from the input motion capture data. This online adjustment produces a physically plausible motion trajectory adapted to dynamic environments, which is then used as the desired motion for the motion controllers to track in dynamics simulation. Rather than using Proportional-Derivative controllers whose parameters usually cannot be easily set, our motion tracking adopts a velocity-driven method which computes joint torques based on the desired joint angular velocities. Physically correct full-body motion of the 3D character is computed in dynamics simulation using the computed torques and dynamical model of the character. Our experiments demonstrate that tracking motion capture data with real-time response animation can be achieved easily. In addition, physically plausible motion style editing, automatic motion transition, and motion adaptation to different limb sizes can also be generated without difficulty.
Social-Event-Driven Camera Control for Multicharacter Animations
(2012-09-01)
Yeh, I-Cheng
;
Lin, Wen-Chieh
;
Lee, Tong-Yee
;
Han, Hsin-Ju
;
Lee, Jehee
"In a virtual world, a group of virtual characters can interact with each other, and these characters may leave a group to join another. The interaction among individuals and groups often produces interesting events in a sequence of animation. The goal of this paper is to discover social events involving mutual interactions or group activities in multicharacter animations and automatically plan a smooth camera motion to view interesting events suggested by our system or relevant events specified by a user. Inspired by sociology studies, we borrow the knowledge in Proxemics, social force, and social network analysis to model the dynamic relation among social events and the relation among the participants within each event. By analyzing the variation of relation strength among participants and spatiotemporal correlation among events, we discover salient social events in a motion clip and generate an overview video of these events with smooth camera motion using a simulated annealing optimization method. We tested our approach on different motions performed by multiple characters. Our user study shows that our results are preferred in 66.19 percent of the comparisons with those by the camera control approach without event analysis and are comparable (51.79 percent) to professional results by an artist."