Emotion Transfer for 3D Hand Motion using StarGAN

Chan, Jacky C. P., Irimia, Ana-Sabina and Ho, Edmond (2020) Emotion Transfer for 3D Hand Motion using StarGAN. In: Computer Graphics & Visual Computing (CGVC) 2020. The Eurographics Association, Geneva, p. 20201146. ISBN 9783038681229

Text (Proof)
CGVC2020_Final_Emotion_Transfer_for_3D_Hand_Motion_using_StarGAN_low.pdf - Other

Download (1MB) | Preview
Official URL: https://doi.org/10.2312/cgvc.20201146


In this paper, we propose a new data-driven framework for 3D hand motion emotion transfer. Specifically, we first capture high-quality hand motion using VR gloves. The hand motion data is then annotated with the emotion type and converted to images to facilitate the motion synthesis process and the new dataset will be available to the public. To the best of our knowledge, this is the first public dataset with annotated hand motions. We further formulate the emotion transfer for 3D hand motion as an Image-to-Image translation problem, and it is done by adapting the StarGAN framework. Our new framework is able to synthesize new motions, given target emotion type and an unseen input motion. Experimental results show that our framework can produce high quality and consistent hand motions.

Item Type: Book Section
Uncontrolled Keywords: hand animation, emotion, motion capture, generative adversarial network, style transfer
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: John Coen
Date Deposited: 12 Aug 2020 13:28
Last Modified: 31 Jul 2021 10:34
URI: http://nrl.northumbria.ac.uk/id/eprint/44069

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics