Digital watermarking is widely used for copyright protection. Traditional 3D
watermarking approaches or commercial software are typically designed to embed
messages into 3D meshes, and later retrieve the messages directly from
distorted/undistorted watermarked 3D meshes. Retrieving messages from 2D
renderings of such meshes, however, is still challenging and underexplored. We
introduce a novel end-to-end learning framework to solve this problem through:
1) an encoder to covertly embed messages in both mesh geometry and textures; 2)
a differentiable renderer to render watermarked 3D objects from different
camera angles and under varied lighting conditions; 3) a decoder to recover the
messages from 2D rendered images. From extensive experiments, we show that our
models learn to embed information visually imperceptible to humans, and to
reconstruct the embedded information from 2D renderings robust to 3D
distortions. In addition, we demonstrate that our method can be generalized to
work with different renderers, such as ray tracers and real-time renderers.

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