3D virtual try-on enjoys many potential applications and hence has attracted
wide attention. However, it remains a challenging task that has not been
adequately solved. Existing 2D virtual try-on methods cannot be directly
extended to 3D since they lack the ability to perceive the depth of each pixel.
Besides, 3D virtual try-on approaches are mostly built on the fixed topological
structure and with heavy computation. To deal with these problems, we propose a
Decomposed Implicit garment transfer network (DI-Net), which can effortlessly
reconstruct a 3D human mesh with the newly try-on result and preserve the
texture from an arbitrary perspective. Specifically, DI-Net consists of two
modules: 1) A complementary warping module that warps the reference image to
have the same pose as the source image through dense correspondence learning
and sparse flow learning; 2) A geometry-aware decomposed transfer module that
decomposes the garment transfer into image layout based transfer and texture
based transfer, achieving surface and texture reconstruction by constructing
pixel-aligned implicit functions. Experimental results show the effectiveness
and superiority of our method in the 3D virtual try-on task, which can yield
more high-quality results over other existing methods.

3D 虚拟试穿是一个具有广泛潜力且备受关注的挑战性任务，本文提出了一种基于分解的隐式服装转换网络 (DI-Net)，通过像素对齐的隐式函数构建方法，实现了从任意视角重建 3D 人体网格和保留其纹理恢复的能力，在 3D 虚拟试穿任务中展现出优异的性能和效果。

DI-Net：解耦式隐式服装转移网络用于数字化人类的服饰三维模型

DI-Net : Decomposed Implicit Garment Transfer Network for Digital  Clothed 3D Human

While neural fields have emerged as powerful representations of continuous
data, there is a need for neural networks that can perform inference on such
data without being sensitive to how the field is sampled, a property called
discretization invariance. We develop DI-Net, a framework for learning
discretization invariant operators on neural fields of any type. Whereas
current theoretical analyses of discretization invariant networks are
restricted to the limit of infinite samples, our analysis does not require
infinite samples and establishes upper bounds on the variation in DI-Net
outputs given different finite discretizations. Our framework leads to a family
of neural networks driven by numerical integration via quasi-Monte Carlo
sampling with discretizations of low discrepancy. DI-Nets manifest desirable
theoretical properties such as universal approximation of a large class of maps
between $L^2$ functions, and gradients that are also discretization invariant.
DI-Nets can also be seen as generalizations of many existing network families
as they bridge discrete and continuous network classes, such as convolutional
neural networks (CNNs) and neural operators respectively. Experimentally,
DI-Nets derived from CNNs can learn to classify and segment visual data
represented by neural fields under various discretizations, and sometimes even
generalize to new types of discretizations at test time. Code:
this https URL

本文提出了 DI-Net，一种对任何类型的神经场进行学习离散化不变算子的框架，使用基于数值积分和低偏差的拟随机采样技术，DI-Net 达到了一系列良好的理论性质和实验结果，将现有网络家族，如 CNN 和神经算符等，从离散到连续类进行了推广。