Chung-yeon Lee

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Learning from human behaviors in the real world is important for building human-aware intelligent systems such as personalized digital assistants and autonomous humanoid robots. Everyday activities of human life can now be measured through wearable sensors. However, innovations are required to learn these sensory data in an online in-cremental manner over(More)
An intriguing tactic for achieving facial expressions beyond realistic approaches has gradually drawn lots of attention in the fields of video games, avatars, teleconference, human-computer interface and computer animation. However, most researches on facial expressions tend to remain faithful to the trend in realistic expressions rather than exaggerating(More)
Understanding episodic memory formation of real-world events is essential for the investigation of human cognition. Most studies have stressed on delimiting the upper boundaries of this memory by using memorization tasks with conditional experimental paradigms, rather than the performance of everyday tasks. However, naturally occurring sensory stimuli are(More)
This paper presents a method of shoal motion with an effective leadership of autonomous virtual fish. Shoal motion is led by a leader and computed by five steering behavior vectors including cohesion, separation, velocity, escape and goal vectors. Through experiments, we demonstrate that a leader of the motion simulation has great effect in leadership and(More)
Episodic memory formation is associated with large-scale neuronal activity distributed across the cortex. Decades of neuroimaging and patient lesion studies demonstrated the correlation between the roles of specific brain structures in episodic memory retrieval. Distributed, coordinated and synchronized activities across brain regions have also been(More)
Facial expressions have always attracted considerable attention as a form of nonverbal communication. In visual applications such as movies, games, and animations, people tend to be interested in exaggerated expressions rather than regular expressions since the exaggerated ones deliver more vivid emotions. In this paper, we propose an automatic method for(More)