MeshDiffusion：基于评分的生成式3D网格建模

我们考虑生成逼真的三维形状的任务，这在自动生成场景和物理模拟等多种应用中非常有用。与 Voxels 和点云等其他三维表示相比，网格在实践中更可取，因为 (1)它们使得形状的灯光照射和模拟易于任意操作，(2)它们可以充分利用现代图形管线的强大能力，这些管线大多数都是针对网格进行优化的。以前用于生成网格的可扩展方法通常依赖于次优后处理，它们倾向于产生过度平滑或嘈杂的表面，缺乏精细的几何细节。为了克服这些缺点，我们利用网格的图形结构，采用一种简单但非常有效的生成建模方法来生成三维网格。具体来说，我们用可变形四面体网格来表示网格，然后在这个直接参数化上训练扩散模型。我们展示了我们的模型在多个生成任务上的有效性。
We consider the task of generating realistic 3D shapes, which is useful for a
variety of applications such as automatic scene generation and physical
simulation. Compared to other 3D representations like voxels and point clouds,
meshes are more desirable in practice, because (1) they enable easy and
arbitrary manipulation of shapes for relighting and simulation, and (2) they
can fully leverage the power of modern graphics pipelines which are mostly
optimized for meshes. Previous scalable methods for generating meshes typically
rely on sub-optimal post-processing, and they tend to produce overly-smooth or
noisy surfaces without fine-grained geometric details. To overcome these
shortcomings, we take advantage of the graph structure of meshes and use a
simple yet very effective generative modeling method to generate 3D meshes.
Specifically, we represent meshes with deformable tetrahedral grids, and then
train a diffusion model on this direct parametrization. We demonstrate the
effectiveness of our model on multiple generative tasks.
论文链接：http://arxiv.org/pdf/2303.08133v1