Neural Pose Transfer by Spatially Adaptive Instance Normalization
Jiashun Wang, Chao Wen, Yanwei Fu, Haitao Lin, Tianyun Zou, Xiangyang Xue, Yinda Zhang
Abstract
Figure 1: Four groups of pose transfer examples. Each visualization group consists of 3 meshes, input pose mesh, input identity mesh, and our result. For the first two groups, we show the pose mesh from SMPL [21], the identity mesh from FAUST [5] and our result, the identity mesh of the third group is from MG-dataset[4], the last group shows our pose transfer result from the animal dataset [30], please refer to supplementary materials for more details.
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Install the CLIlune papers fulltext 8e75777a-b704-4d9f-9a1d-9aaa0e172f8cCited by top-tier papers17
- TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion SynthesisMathis Petrovich, Michael J. Black, Gül VarolICCV 2023 · 192 citations
- 3D Pose Transfer with Correspondence Learning and Mesh RefinementChaoyue Song, Jiacheng Wei, Ruibo Li, Fayao Liu et al.NeurIPS 2021 · 43 citations
- Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose TransferHaoyu Chen, Hao Tang, Henglin Shi, Wei Peng et al.ICCV 2021 · 33 citations
- Geometry-Contrastive Transformer for Generalized 3D Pose TransferHaoyu Chen, Hao Tang, Zitong Yu, Nicu Sebe et al.AAAI 2022 · 18 citations
- PMVC: Data Augmentation-Based Prosody Modeling for Expressive Voice ConversionYimin Deng, Huaizhen Tang, Xulong Zhang, Jianzong Wang et al.ACM MM 2023 · 15 citations
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