Unsupervised 3D Pose Transfer With Cross Consistency and Dual Reconstruction

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Unsupervised 3D Pose Transfer With Cross Consistency and Dual Reconstruction
Title:
Unsupervised 3D Pose Transfer With Cross Consistency and Dual Reconstruction
Journal Title:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publication Date:
20 March 2023
Citation:
Song, C., Wei, J., Li, R., Liu, F., & Lin, G. (2023). Unsupervised 3D Pose Transfer With Cross Consistency and Dual Reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–13. https://doi.org/10.1109/tpami.2023.3259059
Abstract:
Abstract—The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh while preserving the identity information (e.g., face, body shape) of the target mesh. Deep learning-based methods improved the efficiency and performance of 3D pose transfer. However, most of them are trained under the supervision of the ground truth, whose availability is limited in real-world scenarios. In this work, we present X-DualNet, a simple yet effective approach that enables unsupervised 3D pose transfer. In X-DualNet, we introduce a generator G which contains correspondence learning and pose transfer modules to achieve 3D pose transfer. We learn the shape correspondence by solving an optimal transport problem without any key point annotations and generate high-quality meshes with our elastic instance normalization (ElaIN) in the pose transfer module. With G as the basic component, we propose a cross consistency learning scheme and a dual reconstruction objective to learn the pose transfer without supervision. Besides that, we also adopt an as-rigid-as-possible deformer in the training process to fine-tune the body shape of the generated results. Extensive experiments on human and animal data demonstrate that our framework can successfully achieve comparable performance as the state-of-the-art supervised approaches.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - RIE2020 Industry Alignment Fund
Grant Reference no. : I1901E0052

This research / project is supported by the AI Singapore - AI Singapore Programme
Grant Reference no. : AISG-RP-2018-003

This research / project is supported by the Ministry of Education - Academic Research Fund Tier 2
Grant Reference no. : MOE-T2EP20220-0007

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - MTC Young Individual Research Grant
Grant Reference no. : M21K3c0130
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2160-9292
1939-3539
0162-8828
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