Lu, J, Wang, H orcid.org/0000-0002-2281-5679, Shao, T et al. (2 more authors) (2022) Pose Guided Image Generation from Misaligned Sources via Residual Flow Based Correction. In: Proceedings of the AAAI Conference on Artificial Intelligence. 36th AAAI Conference on Artificial Intelligence, 22 Feb - 01 Mar 2022, Online. AAAI , pp. 1863-1871. ISBN 978-1-57735-876-3
Abstract
Generating new images with desired properties (e.g. new view/poses) from source images has been enthusiastically pursued recently, due to its wide range of potential applications. One way to ensure high-quality generation is to use multiple sources with complementary information such as different views of the same object. However, as source images are often misaligned due to the large disparities among the camera settings, strong assumptions have been made in the past with respect to the camera(s) or/and the object in interest, limiting the application of such techniques. Therefore, we propose a new general approach which models multiple types of variations among sources, such as view angles, poses, facial expressions, in a unified framework, so that it can be employed on datasets of vastly different nature. We verify our approach on a variety of data including humans bodies, faces, city scenes and 3D objects. Both the qualitative and quantitative results demonstrate the better performance of our method than the state of the art.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This is an author produced version of a conference paper published in Proceedings of the AAAI Conference on Artificial Intelligence. Uploaded in accordance with the publisher's self-archiving policy. |
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 Jan 2022 18:51 |
Last Modified: | 03 Aug 2023 14:39 |
Status: | Published |
Publisher: | AAAI |
Identification Number: | 10.1609/aaai.v36i2.20080 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182434 |