  Robust 3D mesh watermarking is a traditional research topic in computer
graphics, which provides an efficient solution to the copyright protection for
3D meshes. Traditionally, researchers need manually design watermarking
algorithms to achieve sufficient robustness for the actual application
scenarios. In this paper, we propose the first deep learning-based 3D mesh
watermarking framework, which can solve this problem once for all. In detail,
we propose an end-to-end network, consisting of a watermark embedding
sub-network, a watermark extracting sub-network and attack layers. We adopt the
topology-agnostic graph convolutional network (GCN) as the basic convolution
operation for 3D meshes, so our network is not limited by registered meshes
(which share a fixed topology). For the specific application scenario, we can
integrate the corresponding attack layers to guarantee adaptive robustness
against possible attacks. To ensure the visual quality of watermarked 3D
meshes, we design a curvature-based loss function to constrain the local
geometry smoothness of watermarked meshes. Experimental results show that the
proposed method can achieve more universal robustness and faster watermark
embedding than baseline methods while guaranteeing comparable visual quality.
