Title: Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network,
Abstract: Automatic mesh-based shape generation is of great interest across a wide
range of disciplines, from industrial design to gaming, computer graphics and
various other forms of digital art. While most traditional methods focus on
primitive based model generation, advances in deep learning made it possible to
learn 3-dimensional geometric shape representations in an end-to-end manner.
However, most current deep learning based frameworks focus on the
representation and generation of voxel and point-cloud based shapes, making it
not directly applicable to design and graphics communities. This study
addresses the needs for automatic generation of mesh-based geometries, and
propose a novel framework that utilizes signed distance function representation
that generates detail preserving three-dimensional surface mesh by a deep
learning based approach.