Currently it requires an artist to create 3D human avatars with realistic
clothing that can move naturally. Despite progress on 3D scanning and modeling
of human bodies, there is still no technology that can easily turn a static
scan into an animatable avatar. Automating the creation of such avatars would
enable many applications in games, social networking, animation, and AR/VR to
name a few. The key problem is one of representation. Standard 3D meshes are
widely used in modeling the minimally-clothed body but do not readily capture
the complex topology of clothing. Recent interest has shifted to implicit
surface models for this task but they are computationally heavy and lack
compatibility with existing 3D tools. What is needed is a 3D representation
that can capture varied topology at high resolution and that can be learned
from data. We argue that this representation has been with us all along -- the
point cloud. Point clouds have properties of both implicit and explicit
representations that we exploit to model 3D garment geometry on a human body.
We train a neural network with a novel local clothing geometric feature to
represent the shape of different outfits. The network is trained from 3D point
clouds of many types of clothing, on many bodies, in many poses, and learns to
model pose-dependent clothing deformations. The geometry feature can be
optimized to fit a previously unseen scan of a person in clothing, enabling the
scan to be reposed realistically. Our model demonstrates superior quantitative
and qualitative results in both multi-outfit modeling and unseen outfit
animation. The code is available for research purposes.

该研究使用 3D 点云与神经网络技术，通过学习服装的形状特征和姿态变形，并优化适应未见数据，实现了高效的 3D 人体化身动态生成。

使用点的能力对穿着衣物的人进行建模

The Power of Points for Modeling Humans in Clothing

Animating a newly designed character using motion capture (mocap) data is a
long standing problem in computer animation. A key consideration is the
skeletal structure that should correspond to the available mocap data, and the
shape deformation in the joint regions, which often requires a tailored,
pose-specific refinement. In this work, we develop a neural technique for
articulating 3D characters using enveloping with a pre-defined skeletal
structure which produces high quality pose dependent deformations. Our
framework learns to rig and skin characters with the same articulation
structure (e.g., bipeds or quadrupeds), and builds the desired skeleton
hierarchy into the network architecture. Furthermore, we propose neural blend
shapes--a set of corrective pose-dependent shapes which improve the deformation
quality in the joint regions in order to address the notorious artifacts
resulting from standard rigging and skinning. Our system estimates neural blend
shapes for input meshes with arbitrary connectivity, as well as weighting
coefficients which are conditioned on the input joint rotations. Unlike recent
deep learning techniques which supervise the network with ground-truth rigging
and skinning parameters, our approach does not assume that the training data
has a specific underlying deformation model. Instead, during training, the
network observes deformed shapes and learns to infer the corresponding rig,
skin and blend shapes using indirect supervision. During inference, we
demonstrate that our network generalizes to unseen characters with arbitrary
mesh connectivity, including unrigged characters built by 3D artists.
Conforming to standard skeletal animation models enables direct plug-and-play
in standard animation software, as well as game engines.

本文通过开发一种基于神经技术的方法，在不假定训练数据的基础形变模型的情况下，使神经网络学习到骨架、皮肤和混合形状，并构建了一个骨骼层次结构的网络架构，利用预定义的骨骼结构使 3D 角色实现高质量的姿态相关形变，其中提出了用于改进关节区域变形质量的纠正姿势依赖形状的方法。

神经混合形态学习骨架关节

Learning Skeletal Articulations with Neural Blend Shapes

Substantial progress has been made on modeling rigid 3D objects using deep
implicit representations. Yet, extending these methods to learn neural models
of human shape is still in its infancy. Human bodies are complex and the key
challenge is to learn a representation that generalizes such that it can
express body shape deformations for unseen subjects in unseen,
highly-articulated, poses. To address this challenge, we introduce LEAP
(LEarning Articulated occupancy of People), a novel neural occupancy
representation of the human body. Given a set of bone transformations (i.e.
joint locations and rotations) and a query point in space, LEAP first maps the
query point to a canonical space via learned linear blend skinning (LBS)
functions and then efficiently queries the occupancy value via an occupancy
network that models accurate identity- and pose-dependent deformations in the
canonical space. Experiments show that our canonicalized occupancy estimation
with the learned LBS functions greatly improves the generalization capability
of the learned occupancy representation across various human shapes and poses,
outperforming existing solutions in all settings.

本文提出了一种名为 LEAP 的新型神经占据表示方法，用于建模复杂的人体形状变形。通过使用学习的 LBS 功能，将查询点映射到规范空间，并通过占据网络对人体的姿势和形状变形进行建模，从而大大提高了学习占据表示的泛化能力。