  We propose DeepMetaHandles, a 3D conditional generative model based on mesh
deformation. Given a collection of 3D meshes of a category and their
deformation handles (control points), our method learns a set of meta-handles
for each shape, which are represented as combinations of the given handles. The
disentangled meta-handles factorize all the plausible deformations of the
shape, while each of them corresponds to an intuitive deformation. A new
deformation can then be generated by sampling the coefficients of the
meta-handles in a specific range. We employ biharmonic coordinates as the
deformation function, which can smoothly propagate the control points'
translations to the entire mesh. To avoid learning zero deformation as
meta-handles, we incorporate a target-fitting module which deforms the input
mesh to match a random target. To enhance deformations' plausibility, we employ
a soft-rasterizer-based discriminator that projects the meshes to a 2D space.
Our experiments demonstrate the superiority of the generated deformations as
well as the interpretability and consistency of the learned meta-handles.
