  We present a learning-based method for interpolating and manipulating 3D
shapes represented as point clouds, that is explicitly designed to preserve
intrinsic shape properties. Our approach is based on constructing a dual
encoding space that enables shape synthesis and, at the same time, provides
links to the intrinsic shape information, which is typically not available on
point cloud data. Our method works in a single pass and avoids expensive
optimization, employed by existing techniques. Furthermore, the strong
regularization provided by our dual latent space approach also helps to improve
shape recovery in challenging settings from noisy point clouds across different
datasets. Extensive experiments show that our method results in more realistic
and smoother interpolations compared to baselines.
