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Synthesizing Motion with Relative Emotion Strength

Edmond S. L. Ho, Hubert P. H. Shum, He Wang and Li Yi
Proceedings of the 2017 ACM SIGGRAPH Asia Workshop on Data-Driven Animation Techniques (D2AT), 2017

Synthesizing Motion with Relative Emotion Strength

Abstract

With the advancement in motion sensing technology, acquiring high-quality human motions for creating realistic character animation is much easier than before. Since motion data itself is not the main obstacle anymore, more and more effort goes into enhancing the realism of character animation, such as motion styles and control. In this paper, we explore a less studied area: the emotion of motions. Unlike previous work which encode emotions into discrete motion style descriptors, we propose a continuous control indicator called motion strength, by controlling which a data-driven approach is presented to synthesize motions with fine control over emotions. Rather than interpolating motion features to synthesize new motion as in existing work, our method explicitly learns a model mapping low-level motion features to the emotion strength. Since the motion synthesis model is learned in the training stage, the computation time required for synthesizing motions at run-time is very low. As a result, our method can be applied to interactive applications such as computer games and virtual reality applications, as well as offline applications such as animation and movie production.

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Citations

BibTeX

@inproceedings{ho17synthesizing,
 author={Ho, Edmond S. L. and Shum, Hubert P. H. and Wang, He and Yi, Li},
 booktitle={Proceedings of the 2017 ACM SIGGRAPH Asia Workshop on Data-Driven Animation Techniques},
 series={D2AT '17},
 title={Synthesizing Motion with Relative Emotion Strength},
 year={2017},
 month={11},
 numpages={8},
 location={Bangkok, Thailand},
}

RIS

TY  - CONF
AU  - Ho, Edmond S. L.
AU  - Shum, Hubert P. H.
AU  - Wang, He
AU  - Yi, Li
T2  - Proceedings of the 2017 ACM SIGGRAPH Asia Workshop on Data-Driven Animation Techniques
TI  - Synthesizing Motion with Relative Emotion Strength
PY  - 2017
Y1  - 11 2017
ER  - 

Plain Text

Edmond S. L. Ho, Hubert P. H. Shum, He Wang and Li Yi, "Synthesizing Motion with Relative Emotion Strength," in D2AT '17: Proceedings of the 2017 ACM SIGGRAPH Asia Workshop on Data-Driven Animation Techniques, Bangkok, Thailand, Nov 2017.

Supporting Grants

Similar Research

Jacky C. P. Chan, Hubert P. H. Shum, He Wang, Li Yi, Wei Wei and Edmond S. L. Ho, "A Generic Framework for Editing and Synthesizing Multimodal Data with Relative Emotion Strength", Computer Animation and Virtual Worlds (CAVW), 2019
He Wang, Edmond S. L. Ho, Hubert P. H. Shum and Zhanxing Zhu, "Spatio-Temporal Manifold Learning for Human Motions via Long-Horizon Modeling", IEEE Transactions on Visualization and Computer Graphics (TVCG), 2021
Hubert P. H. Shum, Ludovic Hoyet, Edmond S. L. Ho, Taku Komura and Franck Multon, "Preparation Behaviour Synthesis with Reinforcement Learning", Proceedings of the 2013 International Conference on Computer Animation and Social Agents (CASA), 2013
Liuyang Zhou, Lifeng Shang, Hubert P. H. Shum and Howard Leung, "Human Motion Variation Synthesis with Multivariate Gaussian Processes", Computer Animation and Virtual Worlds (CAVW) - Proceedings of the 2014 International Conference on Computer Animation and Social Agents (CASA), 2014
Hubert P. H. Shum, Ludovic Hoyet, Edmond S. L. Ho, Taku Komura and Franck Multon, "Natural Preparation Behavior Synthesis", Computer Animation and Virtual Worlds (CAVW), 2013
Hubert P. H. Shum, Taku Komura and Pranjul Yadav, "Angular Momentum Guided Motion Concatenation", Computer Animation and Virtual Worlds (CAVW) - Proceedings of the 2009 International Conference on Computer Animation and Social Agents (CASA), 2009
Edmund J. C. Findlay, Haozheng Zhang, Ziyi Chang and Hubert P. H. Shum, "Denoising Diffusion Probabilistic Models for Styled Walking Synthesis", Proceedings of the 2022 ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG) Posters, 2022
Naoya Iwamoto, Hubert P. H. Shum, Wakana Asahina and Shigeo Morishima, "Automatic Sign Dance Synthesis from Gesture-Based Sign Language", Proceedings of the 2019 ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG), 2019
Wakana Asahina, Naoya Iwamoto, Hubert P. H. Shum and Shigeo Morishima, "Automatic Dance Generation System Considering Sign Language Information", Proceedings of the 2016 ACM SIGGRAPH Posters, 2016

 

 

Last updated on 24 February 2024
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