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• W4309505596 abstract "Acquiring the virtual equivalent of exhibits, such as sculptures, in virtual reality (VR) museums, can be labour-intensive and sometimes infeasible. Deep learning based 3D reconstruction approaches allow us to recover 3D shapes from 2D observations, among which single-view-based approaches can reduce the need for human intervention and specialised equipment in acquiring 3D sculptures for VR museums. However, there exist two challenges when attempting to use the well-researched human reconstruction methods: limited data availability and domain shift. Considering sculptures are usually related to humans, we propose our unsupervised 3D domain adaptation method for adapting a single-view 3D implicit reconstruction model from the source (real-world humans) to the target (sculptures) domain. We have compared the generated shapes with other methods and conducted ablation studies as well as a user study to demonstrate the effectiveness of our adaptation method. We also deploy our results in a VR application." @default.
• W4309505596 created "2022-11-28" @default.
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• W4309505596 date "2022-11-29" @default.
• W4309505596 modified "2023-09-27" @default.
• W4309505596 title "3D Reconstruction of Sculptures from Single Images via Unsupervised Domain Adaptation on Implicit Models" @default.
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• W4309505596 doi "https://doi.org/10.1145/3562939.3565632" @default.
• W4309505596 hasPublicationYear "2022" @default.
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