Geng, J, Shao, T, Zheng, Y et al. (2 more authors) (2018) Warp-Guided GANs for Single-Photo Facial Animation. ACM Transactions on Graphics, 37 (6). ARTN 231. ISSN 0730-0301
Abstract
This paper introduces a novel method for realtime portrait animation in a single photo. Our method requires only a single portrait photo and a set of facial landmarks derived from a driving source (e.g., a photo or a video sequence), and generates an animated image with rich facial details. The core of our method is a warp-guided generative model that instantly fuses various fine facial details (e.g., creases and wrinkles), which are necessary to generate a high-fidelity facial expression, onto a pre-warped image. Our method factorizes out the nonlinear geometric transformations exhibited in facial expressions by lightweight 2D warps and leaves the appearance detail synthesis to conditional generative neural networks for high-fidelity facial animation generation. We show such a factorization of geometric transformation and appearance synthesis largely helps the network better learn the high nonlinearity of the facial expression functions and also facilitates the design of the network architecture. Through extensive experiments on various portrait photos from the Internet, we show the significant efficacy of our method compared with prior arts.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author’s version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Graphics, https://doi.org/10.1145/3272127.3275043. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Nov 2018 16:00 |
Last Modified: | 30 Jan 2019 14:20 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Identification Number: | 10.1145/3272127.3275043 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:138578 |