Gu, Yajie, Pears, N. E. orcid.org/0000-0001-9513-5634 and Sun, Hao (Accepted: 2022) Adversarial 3D Face Disentanglement of Identity and Expression. In: International Conference on Automatic Face and Gesture Recognition 2023. International Conference on Automatic Face and Gesture Recognition 2023, 05-08 Jan 2023 IEEE , USA (In Press)
Abstract
We propose a new framework to decompose 3D facial shape into identity and expression. Existing 3D face disentanglement methods assume the presence of a corresponding neutral (i.e. identity) face for each subject. Our method designs an identity discriminator to obviate this requirement. This is a binary classifier that determines if two input faces are from the same identity, and encourages the synthesised identity face to have the same identity features as the input face and to approach the `apathy' expression. To this end, we take advantage of adversarial learning to train a PointNet-based variational auto-encoder and discriminator. Comprehensive experiments are employed on CoMA, BU3DFE, and FaceScape datasets. Results demonstrate state-of-the-art performance with the option of operating in a more versatile application setting of no known neutral ground truths. Code is available at \url{https://github.com/rmraaron/FaceExpDisentanglement}.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 18 Nov 2022 12:10 |
Last Modified: | 16 Oct 2024 11:21 |
Status: | In Press |
Publisher: | IEEE |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193526 |
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