Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162762
Title: Facial expression retargeting from human to avatar made easy
Authors: Zhang, Juyong
Chen, Keyu
Zheng, Jianmin
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Zhang, J., Chen, K. & Zheng, J. (2020). Facial expression retargeting from human to avatar made easy. IEEE Transactions On Visualization and Computer Graphics, 28(2), 1274-1287. https://dx.doi.org/10.1109/TVCG.2020.3013876
Project: 04INS000518C130
MOE 2017-T2-1- 076
Journal: IEEE Transactions on Visualization and Computer Graphics 
Abstract: Facial expression retargeting from humans to virtual characters is a useful technique in computer graphics and animation. Traditional methods use markers or blendshapes to construct a mapping between the human and avatar faces. However, these approaches require a tedious 3D modeling process, and the performance relies on the modelers' experience. In this article, we propose a brand-new solution to this cross-domain expression transfer problem via nonlinear expression embedding and expression domain translation. We first build low-dimensional latent spaces for the human and avatar facial expressions with variational autoencoder. Then we construct correspondences between the two latent spaces guided by geometric and perceptual constraints. Specifically, we design geometric correspondences to reflect geometric matching and utilize a triplet data structure to express users' perceptual preference of avatar expressions. A user-friendly method is proposed to automatically generate triplets for a system allowing users to easily and efficiently annotate the correspondences. Using both geometric and perceptual correspondences, we trained a network for expression domain translation from human to avatar. Extensive experimental results and user studies demonstrate that even nonprofessional users can apply our method to generate high-quality facial expression retargeting results with less time and effort.
URI: https://hdl.handle.net/10356/162762
ISSN: 1077-2626
DOI: 10.1109/TVCG.2020.3013876
Rights: © 2020 IEEE. All rights reserved
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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