  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.
