In this paper, we present TailorNet, a neural model which predicts clothing
deformation in 3D as a function of three factors: pose, shape and style
(garment geometry), while retaining wrinkle detail. This goes beyond prior
models, which are either specific to one style and shape, or generalize to
different shapes producing smooth results, despite being style specific. Our
hypothesis is that (even non-linear) combinations of examples smooth out high
frequency components such as fine-wrinkles, which makes learning the three
factors jointly hard. At the heart of our technique is a decomposition of
deformation into a high frequency and a low frequency component. While the
low-frequency component is predicted from pose, shape and style parameters with
an MLP, the high-frequency component is predicted with a mixture of shape-style
specific pose models. The weights of the mixture are computed with a narrow
bandwidth kernel to guarantee that only predictions with similar high-frequency
patterns are combined. The style variation is obtained by computing, in a
canonical pose, a subspace of deformation, which satisfies physical constraints
such as inter-penetration, and draping on the body. TailorNet delivers 3D
garments which retain the wrinkles from the physics based simulations (PBS) it
is learned from, while running more than 1000 times faster. In contrast to PBS,
TailorNet is easy to use and fully differentiable, which is crucial for
computer vision algorithms. Several experiments demonstrate TailorNet produces
more realistic results than prior work, and even generates temporally coherent
deformations on sequences of the AMASS dataset, despite being trained on static
poses from a different dataset. To stimulate further research in this
direction, we will make a dataset consisting of 55800 frames, as well as our
model publicly available at this https URL

TailorNet 是一个用于预测三维服装变形的神经模型，它综合考虑了服装的姿态、形状和风格等因素，同时保留了皱纹细节。该模型采用了分解变形模型的方法，通过低频和高频组合预测服装形变，并且相比之前的模型，TailorNet 可以更快地提供更加真实的结果。