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. However, in many cases, users only
have access to rendered 2D images instead of 3D meshes. Unfortunately,
retrieving messages from 2D renderings of 3D meshes 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 our experiments, we show
that our model can learn to embed information visually imperceptible to humans,
and to retrieve the embedded information from 2D renderings that undergo 3D
distortions. In addition, we demonstrate that our method can also work with
other renderers, such as ray tracers and real-time renderers with and without
fine-tuning.

本研究提出了一种新的端到端学习框架，通过深度嵌入编码器，在 3D 模型的几何和纹理中实现数字水印嵌入，利用可微渲染器从不同角度和光照条件下渲染模型，并通过解码器从 2D 渲染图像中恢复信息。实验证明，该模型可以从扭曲 / 未扭曲的水印 3D 网格中检索由人类视觉无法察觉的信息，并且可以在其他渲染器中使用。