인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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
- 저자정보
피인용 0
초록·키워드
Intelligent science and technology are constantly changing, and society and people’s lives are inseparable from science and technology, affecting the transformation of artistic creation. This study constructed a choreography model based on a mixed-density network and action feature filtering strategy to help solve the disadvantages of traditional choreography. The model combines the long short-term memory (LSTM) network and mixed-density network to generate dance movements. First, the LSTM gating mechanism was used to learn the characteristics of human dance movements and obtain the conversion rules of various poses. The mixed-density network was introduced to compensate for the uncontrollable probability distribution in LSTM. In addition, during action generation, the experiment focused on the continuity between adjacent actions to screen the generated dance actions according to the diversity of dance actions and to enhance the continuity and authenticity of dance. Finally, an experiment was conducted on the spatial feature extraction and music feature matching of the model to achieve the goal that the model can generate different styles of choreography. The test user gave a score of more than four points to the final choreography effects of different styles, showing that the model can achieve a better intelligent choreography effect.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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