Parametric 3D body models like SMPL only represent minimally-clothed people
and are hard to extend to clothing because they have a fixed mesh topology and
resolution. To address these limitations, recent work uses implicit surfaces or
point clouds to model clothed bodies. While not limited by topology, such
methods still struggle to model clothing that deviates significantly from the
body, such as skirts and dresses. This is because they rely on the body to
canonicalize the clothed surface by reposing it to a reference shape.
Unfortunately, this process is poorly defined when clothing is far from the
body. Additionally, they use linear blend skinning to pose the body and the
skinning weights are tied to the underlying body parts. In contrast, we model
the clothing deformation in a local coordinate space without canonicalization.
We also relax the skinning weights to let multiple body parts influence the
surface. Specifically, we extend point-based methods with a coarse stage, that
replaces canonicalization with a learned pose-independent "coarse shape" that
can capture the rough surface geometry of clothing like skirts. We then refine
this using a network that infers the linear blend skinning weights and pose
dependent displacements from the coarse representation. The approach works well
for garments that both conform to, and deviate from, the body. We demonstrate
the usefulness of our approach by learning person-specific avatars from
examples and then show how they can be animated in new poses and motions. We
also show that the method can learn directly from raw scans with missing data,
greatly simplifying the process of creating realistic avatars. Code is
available for research purposes at
{\smallhttps://qianlim.github.io/SkiRT}.

该研究通过使用可以捕捉衣物粗糙表面几何特征的学习的非规范化本地变形来解决现有点云或隐式表面建模方法对衣服建模的不足，可以用于学习人物特定的化身并演示其动画效果，并能从原始扫描直接学习，使得创建逼真化身的过程大大简化。

具有挑战性服装的人体神经点基形状建模

Neural Point-based Shape Modeling of Humans in Challenging Clothing

This paper describes how to obtain accurate 3D body models and texture of
arbitrary people from a single, monocular video in which a person is moving.
Based on a parametric body model, we present a robust processing pipeline
achieving 3D model fits with 5mm accuracy also for clothed people. Our main
contribution is a method to nonrigidly deform the silhouette cones
corresponding to the dynamic human silhouettes, resulting in a visual hull in a
common reference frame that enables surface reconstruction. This enables
efficient estimation of a consensus 3D shape, texture and implanted animation
skeleton based on a large number of frames. We present evaluation results for a
number of test subjects and analyze overall performance. Requiring only a
smartphone or webcam, our method enables everyone to create their own fully
animatable digital double, e.g., for social VR applications or virtual try-on
for online fashion shopping.

该论文介绍了如何从单目视频中获取任意人物的准确三维身体模型和纹理，该方法仅需使用智能手机或网络摄像头，使每个人都能创建自己的全可动数字双胞胎，如社交 VR 应用或在线时尚购物的虚拟试穿。