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MienCap: Performance-based Facial Animation with Live Mood Dynamics

Pan, Ye; Zhang, Ruisi; Wang, Jingying; Chen, Nengfu; Qiu, Yilin; Ding, Yu; Mitchell, Kenny

Authors

Ye Pan

Ruisi Zhang

Jingying Wang

Nengfu Chen

Yilin Qiu

Yu Ding



Abstract

Our purpose is to improve performance-based animation which can drive believable 3D stylized characters that are truly perceptual. By combining traditional blendshape animation techniques with machine learning models, we present a real time motion capture system, called MienCap, which drive character expressions in a geometrically consistent and perceptually valid way. We demon-strate the effectiveness of our system by comparing to a commercial product Faceware. Results reveal that ratings of the recognition of expressions depicted for animated characters via our systems are statistically higher than Faceware. Our results may be implemented into the VR filmmaking and animation pipeline, and provide animators with a system for creating the expressions they wish to use more quickly and accurately.

Citation

Pan, Y., Zhang, R., Wang, J., Chen, N., Qiu, Y., Ding, Y., & Mitchell, K. (2022, March). MienCap: Performance-based Facial Animation with Live Mood Dynamics. Presented at 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Christchurch, New Zealand

Presentation Conference Type Conference Paper (Published)
Conference Name 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
Start Date Mar 12, 2022
End Date Mar 16, 2022
Online Publication Date Apr 20, 2022
Publication Date 2022-03
Deposit Date Jul 15, 2022
Publisher Institute of Electrical and Electronics Engineers
Pages 645-646
Book Title 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
DOI https://doi.org/10.1109/vrw55335.2022.00178
Public URL http://researchrepository.napier.ac.uk/Output/2889043