We propose a method to create plausible geometric and texture style
variations of 3D objects in the quest to democratize 3D content creation. Given
a pair of textured source and target objects, our method predicts a part-aware
affine transformation field that naturally warps the source shape to imitate
the overall geometric style of the target. In addition, the texture style of
the target is transferred to the warped source object with the help of a
multi-view differentiable renderer. Our model, 3DStyleNet, is composed of two
sub-networks trained in two stages. First, the geometric style network is
trained on a large set of untextured 3D shapes. Second, we jointly optimize our
geometric style network and a pre-trained image style transfer network with
losses defined over both the geometry and the rendering of the result. Given a
small set of high-quality textured objects, our method can create many novel
stylized shapes, resulting in effortless 3D content creation and style-ware
data augmentation. We showcase our approach qualitatively on 3D content
stylization, and provide user studies to validate the quality of our results.
In addition, our method can serve as a valuable tool to create 3D data
augmentations for computer vision tasks. Extensive quantitative analysis shows
that 3DStyleNet outperforms alternative data augmentation techniques for the
downstream task of single-image 3D reconstruction.

该研究提出了一种创建三维内容、进行几何和贴图风格变化的方法，借助预先训练的图像样式转换网络和自己的几何样式网络，可实现数据增强与单图像 3D 重构任务。

3DStyleNet：以几何和纹理风格变化创建 3D 形状

3DStyleNet: Creating 3D Shapes with Geometric and Texture Style  Variations

Learning to encode differences in the geometry and (topological) structure of
the shapes of ordinary objects is key to generating semantically plausible
variations of a given shape, transferring edits from one shape to another, and
many other applications in 3D content creation. The common approach of encoding
shapes as points in a high-dimensional latent feature space suggests treating
shape differences as vectors in that space. Instead, we treat shape differences
as primary objects in their own right and propose to encode them in their own
latent space. In a setting where the shapes themselves are encoded in terms of
fine-grained part hierarchies, we demonstrate that a separate encoding of shape
deltas or differences provides a principled way to deal with inhomogeneities in
the shape space due to different combinatorial part structures, while also
allowing for compactness in the representation, as well as edit abstraction and
transfer. Our approach is based on a conditional variational autoencoder for
encoding and decoding shape deltas, conditioned on a source shape. We
demonstrate the effectiveness and robustness of our approach in multiple shape
modification and generation tasks, and provide comparison and ablation studies
on the PartNet dataset, one of the largest publicly available 3D datasets.

本研究提出了一种以不同形状之间的形状差异作为主要对象并将其编码到自己的潜在空间中的方法来进行 3D 内容创建，该方法基于条件变分自编码器，对形状进行编码和解码，以实现对形状差分的压缩表示，分层部分结构的不均匀性以及修改抽象和转移。

StructEdit：学习结构形状变化

StructEdit: Learning Structural Shape Variations

3D content creation is referred to as one of the most fundamental tasks of
computer graphics. And many 3D modeling algorithms from 2D images or curves
have been developed over the past several decades. Designers are allowed to
align some conceptual images or sketch some suggestive curves, from front,
side, and top views, and then use them as references in constructing a 3D model
automatically or manually. However, to the best of our knowledge, no studies
have investigated on 3D human body reconstruction in a similar manner. In this
paper, we propose a deep learning based reconstruction of 3D human body shape
from 2D orthographic views. A novel CNN-based regression network, with two
branches corresponding to frontal and lateral views respectively, is designed
for estimating 3D human body shape from 2D mask images. We train our networks
separately to decouple the feature descriptors which encode the body parameters
from different views, and fuse them to estimate an accurate human body shape.
In addition, to overcome the shortage of training data required for this
purpose, we propose some significantly data augmentation schemes for 3D human
body shapes, which can be used to promote further research on this topic.
Extensive experimen- tal results demonstrate that visually realistic and
accurate reconstructions can be achieved effectively using our algorithm.
Requiring only binary mask images, our method can help users create their own
digital avatars quickly, and also make it easy to create digital human body for
3D game, virtual reality, online fashion shopping.

本文提出了一种基于深度学习的方法，通过 2D 正交视图来实现 3D 人体形态的重建，使用二进制掩膜图像即可快速创建数字化人物，并可应用于 3D 游戏、虚拟现实、在线时装购物等领域。