  The use of autoencoders for shape editing or generation through latent space
manipulation suffers from unpredictable changes in the output shape. Our
autoencoder-based method enables intuitive shape editing in latent space by
disentangling latent sub-spaces into style variables and control points on the
surface that can be manipulated independently. The key idea is adding a
Lipschitz-type constraint to the loss function, i.e. bounding the change of the
output shape proportionally to the change in latent space, leading to
interpretable latent space representations. The control points on the surface
that are part of the latent code of an object can then be freely moved,
allowing for intuitive shape editing directly in latent space. We evaluate our
method by comparing to state-of-the-art data-driven shape editing methods. We
further demonstrate the expressiveness of our learned latent space by
leveraging it for unsupervised part segmentation.
