We present a novel learning method to predict the cloth deformation for
skeleton-based characters with a two-stream network. The characters processed
in our approach are not limited to humans, and can be other skeletal-based
representations of non-human targets such as fish or pets. We use a novel
network architecture which consists of skeleton-based and mesh-based residual
networks to learn the coarse and wrinkle features as the overall residual from
the template cloth mesh. Our network is used to predict the deformation for
loose or tight-fitting clothing or dresses. We ensure that the memory footprint
of our network is low, and thereby result in reduced storage and computational
requirements. In practice, our prediction for a single cloth mesh for the
skeleton-based character takes about 7 milliseconds on an NVIDIA GeForce RTX
3090 GPU. Compared with prior methods, our network can generate fine
deformation results with details and wrinkles.

本研究利用基于骨骼和网格的残差网络来预测基于骨骼的角色穿着松散或紧身服装的皱褶，其生成的细节皱褶丰富、精细，且具备低内存和计算要求，单个角色网格的预测时间为 7 毫秒。

基于骨骼的角色面料变形预测算法：双流蒙皮网络

CTSN: Predicting Cloth Deformation for Skeleton-based Characters with a  Two-stream Skinning Network

Current methods for learning realistic and animatable 3D clothed avatars need
either posed 3D scans or 2D images with carefully controlled user poses. In
contrast, our goal is to learn an avatar from only 2D images of people in
unconstrained poses. Given a set of images, our method estimates a detailed 3D
surface from each image and then combines these into an animatable avatar.
Implicit functions are well suited to the first task, as they can capture
details like hair and clothes. Current methods, however, are not robust to
varied human poses and often produce 3D surfaces with broken or disembodied
limbs, missing details, or non-human shapes. The problem is that these methods
use global feature encoders that are sensitive to global pose. To address this,
we propose ICON ("Implicit Clothed humans Obtained from Normals"), which,
instead, uses local features. ICON has two main modules, both of which exploit
the SMPL(-X) body model. First, ICON infers detailed clothed-human normals
(front/back) conditioned on the SMPL(-X) normals. Second, a visibility-aware
implicit surface regressor produces an iso-surface of a human occupancy field.
Importantly, at inference time, a feedback loop alternates between refining the
SMPL(-X) mesh using the inferred clothed normals and then refining the normals.
Given multiple reconstructed frames of a subject in varied poses, we use
SCANimate to produce an animatable avatar from them. Evaluation on the AGORA
and CAPE datasets shows that ICON outperforms the state of the art in
reconstruction, even with heavily limited training data. Additionally, it is
much more robust to out-of-distribution samples, e.g., in-the-wild poses/images
and out-of-frame cropping. ICON takes a step towards robust 3D clothed human
reconstruction from in-the-wild images. This enables creating avatars directly
from video with personalized and natural pose-dependent cloth deformation.

该论文介绍了一种名为 ICON 的方法，该方法使用局部特征来进行 3D 人体建模，可以从不规则的 2D 图像中生成逼真且可动态的人体模型，并可以创建个性化且自然的动态变形的角色。