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
- 학술대회자료
- 저자정보
- 발행연도
- 2013.8
- 수록면
- 331 - 339 (9page)
이용수
초록· 키워드
오류제보하기
Based on sparse control constraints, one difficulty of synthesizing natural human motions is that low-dimensional control information can not be directly used to reconstruct high-dimensional human poses. This paper introduces a novel and powerful local dimensionality reduction approach for synthesizing accurate and natural full-body human motion using sparse control constraints. Our approach is to construct a group of online local dynamic regression models from a pre-captured motion database as a prior to support the sparse constraints for reconstructing full-body human actions. By synthesizing a variety of human motions from as possible as few sparse constraints provided by users, the paper verifies the effectiveness of the proposed approach. Compared with previous statistical models, our model can be used to synthesize more accurate human motions.
목차
Abstract
1. Introduction
2. Related Work
3. Overview of Online Synthesis System
4. Online Synthesis of Human Behaviors
5. Results and Comparisons
6. Conclusion
References
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UCI(KEPA) : I410-ECN-0101-2015-500-000938614