Emotion Control of Unstructured Dance Movements
Date
2017Author
Aristidou, Andreas
Zeng, Qiong
Stavrakis, Efstathios
Yin, KangKang
Cohen-Or, Daniel
Chrysanthou, Yiorgos
Chen, Baoquan
Metadata
Show full item recordAbstract
Motion capture technology has enabled the acquisition of high quality human motions for animating digital characters with extremely high fidelity. However, despite all the advances in motion editing and synthesis, it remains an open problem to modify pre-captured motions that are highly expressive, such as contemporary dances, for stylization and emotionalization. In this work, we present a novel approach for stylizing such motions by using emotion coordinates de ned by the Russell's Circumplex Model (RCM).We extract and analyze a large set of body and motion features, based on the Laban Movement Analysis (LMA), and choose the e ective and consistent features for characterizing emotions of motions. These features provide a mechanism not only for deriving the emotion coordinates of a newly input motion, but also for stylizing the motion to express a di erent emotion without having to reference the training data. Such decoupling of the training data and new input motions eliminates the necessity of manual processing and motion registration. We implement the two-way mapping between the motion features and emotion coordinates through Radial Basis Function (RBF) regression and interpolation, which can stylize freestyle highly dynamic dance movements at interactive rates. Our results and user studies demonstrate the e ectiveness of the stylization framework with a variety of dance movements exhibiting a diverse set of emotions.
BibTeX
@inproceedings {3099564.3099566,
booktitle = {Eurographics/ ACM SIGGRAPH Symposium on Computer Animation},
editor = {Bernhard Thomaszewski and KangKang Yin and Rahul Narain},
title = {{Emotion Control of Unstructured Dance Movements}},
author = {Aristidou, Andreas and Zeng, Qiong and Stavrakis, Efstathios and Yin, KangKang and Cohen-Or, Daniel and Chrysanthou, Yiorgos and Chen, Baoquan},
year = {2017},
publisher = {ACM},
ISSN = {1727-5288},
ISBN = {978-1-4503-5091-4},
DOI = {10.1145/3099564.3099566}
}
booktitle = {Eurographics/ ACM SIGGRAPH Symposium on Computer Animation},
editor = {Bernhard Thomaszewski and KangKang Yin and Rahul Narain},
title = {{Emotion Control of Unstructured Dance Movements}},
author = {Aristidou, Andreas and Zeng, Qiong and Stavrakis, Efstathios and Yin, KangKang and Cohen-Or, Daniel and Chrysanthou, Yiorgos and Chen, Baoquan},
year = {2017},
publisher = {ACM},
ISSN = {1727-5288},
ISBN = {978-1-4503-5091-4},
DOI = {10.1145/3099564.3099566}
}
Related items
Showing items related by title, author, creator and subject.
-
Motion Templates for Automatic Classification and Retrieval of Motion Capture Data
Müller, Meinard; Röder, Tido (The Eurographics Association, 2006)This paper presents new methods for automatic classification and retrieval of motion capture data facilitating the identification of logically related motions scattered in some database. As the main ingredient, we introduce ... -
MotionMaster: Authoring and Choreographing Kung-fu Motions by Sketch Drawings
Li, Qilei L.; Geng, Weidong D.; Yu, Tao; Shen, Xiao Jie; Lau, Newman; Yu, Gino (The Eurographics Association, 2006)Sketch-drawings is an intuitive and comprehensive means of conveying movement ideas in character animation. We proposed a novel sketch-based approach to assisting the authoring and choreographing of Kungfu motions at the ... -
Compression of Human Motion Capture Data Using Motion Pattern Indexing
Gu, Qin; Peng, Jingliang; Deng, Zhigang (The Eurographics Association and Blackwell Publishing Ltd, 2009)In this work, a novel scheme is proposed to compress human motion capture data based on hierarchical structure construction and motion pattern indexing. For a given sequence of 3D motion capture data of human body, the 3D ...