4 research outputs found
DSM-Net: Disentangled Structured Mesh Net for Controllable Generation of Fine Geometry
3D shape generation is a fundamental operation in computer graphics. While
significant progress has been made, especially with recent deep generative
models, it remains a challenge to synthesize high-quality geometric shapes with
rich detail and complex structure, in a controllable manner. To tackle this, we
introduce DSM-Net, a deep neural network that learns a disentangled structured
mesh representation for 3D shapes, where two key aspects of shapes, geometry
and structure, are encoded in a synergistic manner to ensure plausibility of
the generated shapes, while also being disentangled as much as possible. This
supports a range of novel shape generation applications with intuitive control,
such as interpolation of structure (geometry) while keeping geometry
(structure) unchanged. To achieve this, we simultaneously learn structure and
geometry through variational autoencoders (VAEs) in a hierarchical manner for
both, with bijective mappings at each level. In this manner we effectively
encode geometry and structure in separate latent spaces, while ensuring their
compatibility: the structure is used to guide the geometry and vice versa. At
the leaf level, the part geometry is represented using a conditional part VAE,
to encode high-quality geometric details, guided by the structure context as
the condition. Our method not only supports controllable generation
applications, but also produces high-quality synthesized shapes, outperforming
state-of-the-art methods