지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.12
- 수록면
- 16 - 26 (11page)
이용수
초록· 키워드
오류제보하기
The efficient implementation of various physical actions of agents to respond to dynamically changing situations is essential for the simulation of realistic agents and activities in a cyber world. To achieve a maximum diversity of actions and immediate responsiveness to abrupt changes in situations, we have developed an animation technique in which complex actions are recursively constructed by reusing a set of primitive motions, and agents are designed to react in real-time to abrupt ambient changes by computationally satisfying kinematic constraints on body parts with respect to their goals. Our reusing scheme is extended to visualize the procedure of realistic intricate situations involving many concurring events. Our approach based on motion reuse and recursive assembly has clear advantages in motion variability and action diversity with respect to authoring scalability and motion responsiveness compared to conventional monolithic (static) animation techniques. This diversity also serves to accommodate the characteristic unpredictability of events concurring in a situation due to inherent non-determinism of associated conditions. To demonstrate the viability of our approach, we implement several composite and parallel actions in a dynamically changing example situation involving events that were originally independent until coincidentally inter-coupled therein.
목차
ABSTRACT
1. INTRODUCTION
2. RELATED RESEARCH
3. MODELING OF AGENT’S REUSABLE BEHAVIOR
4. ANIMATION OF COMPLEX INTER-EVENT SITUATION BASED ON REAL-TIME REACTION AND ACTION PARALLELISM
5. AUTHORING SCALABILITY AND REAL-TIME ANIMATION FOR MULTI-EVENT SITUATIONS
6. IMPLEMENTATION OF EXAMPLE ACTIONS AND MULTI-EVENT SITUATIONS
7. CONCLUSION
REFERENCES
참고문헌 (35)
참고문헌 신청
J. Lee / 2009 / Modeling Human Actions based on Primitive Action and Class Inheritance
Guiyu Xia / 2017 / Keyframe Extraction for Human Motion Capture Data Based on Joint Kernel Sparse Representation / IEEE Transactions on Industrial Electronics 64(2):1589~1599
Daniel Lewkowicz / 2016 / Real-Time Motion Capture Toolbox (RTMocap): an open-source code for recording 3-D motion kinematics to study action–effect anticipations during motor and social interactions / Behavior Research Methods 48(1):366~380
Richard Roberts / 2018 / Optimal and interactive keyframe selection for motion capture / SIGGRAPH Asia 2018 Technical Briefs :26
William Li / 2016 / Techniques and Approaches in Static Visualization of Motion Capture Data / Proceedings of the 3rd International Symposium on Movement and Computing :14
Guiyu Xia / 2017 / Keyframe Extraction for Human Motion Capture Data Based on Joint Kernel Sparse Representation / IEEE Transactions on Industrial Electronics 64(2):1589~1599
Daniel Lewkowicz / 2016 / Real-Time Motion Capture Toolbox (RTMocap): an open-source code for recording 3-D motion kinematics to study action–effect anticipations during motor and social interactions / Behavior Research Methods 48(1):366~380
Richard Roberts / 2018 / Optimal and interactive keyframe selection for motion capture / SIGGRAPH Asia 2018 Technical Briefs :26
William Li / 2016 / Techniques and Approaches in Static Visualization of Motion Capture Data / Proceedings of the 3rd International Symposium on Movement and Computing :14
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