  Generating compact and sharply detailed 3D meshes poses a significant
challenge for current 3D generative models. Different from extracting dense
meshes from neural representation, some recent works try to model the native
mesh distribution (i.e., a set of triangles), which generates more compact
results as humans crafted. However, due to the complexity and variety of mesh
topology, these methods are typically limited to small datasets with specific
categories and are hard to extend. In this paper, we introduce a generic and
scalable mesh generation framework PivotMesh, which makes an initial attempt to
extend the native mesh generation to large-scale datasets. We employ a
transformer-based auto-encoder to encode meshes into discrete tokens and decode
them from face level to vertex level hierarchically. Subsequently, to model the
complex typology, we first learn to generate pivot vertices as coarse mesh
representation and then generate the complete mesh tokens with the same
auto-regressive Transformer. This reduces the difficulty compared with directly
modeling the mesh distribution and further improves the model controllability.
PivotMesh demonstrates its versatility by effectively learning from both small
datasets like Shapenet, and large-scale datasets like Objaverse and
Objaverse-xl. Extensive experiments indicate that PivotMesh can generate
compact and sharp 3D meshes across various categories, highlighting its great
potential for native mesh modeling.
