While the community of 3D point cloud generation has witnessed a big growth
in recent years, there still lacks an effective way to enable intuitive user
control in the generation process, hence limiting the general utility of such
methods. Since an intuitive way of decomposing a shape is through its parts, we
propose to tackle the task of controllable part-based point cloud generation.
We introduce DiffFacto, a novel probabilistic generative model that learns the
distribution of shapes with part-level control. We propose a factorization that
models independent part style and part configuration distributions, and present
a novel cross diffusion network that enables us to generate coherent and
plausible shapes under our proposed factorization. Experiments show that our
method is able to generate novel shapes with multiple axes of control. It
achieves state-of-the-art part-level generation quality and generates plausible
and coherent shape, while enabling various downstream editing applications such
as shape interpolation, mixing and transformation editing. Code will be made
publicly available.

本文提出一个能够通过对形状的部分进行建模来实现可控部分点云生成的新颖概率生成模型 DiffFacto，通过独立建模每个部分的样式和配置分布的分解方法，以及利用交叉扩散网络生成一致和合理的形状。实验证明该方法在多方面产生良好的效果，并可用于形状插值、混合和变换编辑等不同功能应用。

基于部件的可控性三维点云生成和交叉扩散的 DiffFacto

DiffFacto Controllable Part-Based 3D Point Cloud Generation with Cross  Diffusion

We propose WarpingGAN, an effective and efficient 3D point cloud generation
network. Unlike existing methods that generate point clouds by directly
learning the mapping functions between latent codes and 3D shapes, Warping-GAN
learns a unified local-warping function to warp multiple identical pre-defined
priors (i.e., sets of points uniformly distributed on regular 3D grids) into 3D
shapes driven by local structure-aware semantics. In addition, we also
ingeniously utilize the principle of the discriminator and tailor a stitching
loss to eliminate the gaps between different partitions of a generated shape
corresponding to different priors for boosting quality. Owing to the novel
generating mechanism, WarpingGAN, a single lightweight network after one-time
training, is capable of efficiently generating uniformly distributed 3D point
clouds with various resolutions. Extensive experimental results demonstrate the
superiority of our WarpingGAN over state-of-the-art methods in terms of
quantitative metrics, visual quality, and efficiency. The source code is
publicly available at this https URL.

本文提出了一个有效和高效的三维点云生成网络 ——WarpingGAN，通过学习统一的局部变形函数将多个相同的预定义先验变形成驱动局部结构意识语义的三维形状，从而在一次训练后以单个轻量级网络的形式高效地生成具有各种分辨率的均匀分布的三维点云。