  3D generative models have been recently successful in generating realistic 3D
objects in the form of point clouds. However, most models do not offer
controllability to manipulate the shape semantics of component object parts
without extensive semantic attribute labels or other reference point clouds.
Moreover, beyond the ability to perform simple latent vector arithmetic or
interpolations, there is a lack of understanding of how part-level semantics of
3D shapes are encoded in their corresponding generative latent spaces. In this
paper, we propose 3DLatNav; a novel approach to navigating pretrained
generative latent spaces to enable controlled part-level semantic manipulation
of 3D objects. First, we propose a part-level weakly-supervised shape semantics
identification mechanism using latent representations of 3D shapes. Then, we
transfer that knowledge to a pretrained 3D object generative latent space to
unravel disentangled embeddings to represent different shape semantics of
component parts of an object in the form of linear subspaces, despite the
unavailability of part-level labels during the training. Finally, we utilize
those identified subspaces to show that controllable 3D object part
manipulation can be achieved by applying the proposed framework to any
pretrained 3D generative model. With two novel quantitative metrics to evaluate
the consistency and localization accuracy of part-level manipulations, we show
that 3DLatNav outperforms existing unsupervised latent disentanglement methods
in identifying latent directions that encode part-level shape semantics of 3D
objects. With multiple ablation studies and testing on state-of-the-art
generative models, we show that 3DLatNav can implement controlled part-level
semantic manipulations on an input point cloud while preserving other features
and the realistic nature of the object.
