  Transferring the style from one image onto another is a popular and widely
studied task in computer vision. Yet, style transfer in the 3D setting remains
a largely unexplored problem. To our knowledge, we propose the first
learning-based approach for style transfer between 3D objects based on
disentangled content and style representations. The proposed method can
synthesize new 3D shapes both in the form of point clouds and meshes, combining
the content and style of a source and target 3D model to generate a novel shape
that resembles in style the target while retaining the source content.
Furthermore, we extend our technique to implicitly learn the multimodal style
distribution of the chosen domains. By sampling style codes from the learned
distributions, we increase the variety of styles that our model can confer to
an input shape. Experimental results validate the effectiveness of the proposed
3D style transfer method on a number of benchmarks. The implementation of our
framework will be released upon acceptance.
