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:
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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