DI-Net : Decomposed Implicit Garment Transfer Network for Digital
  Clothed 3D Human
Xiaojing Zhong Yukun Su Zhonghua Wu Guosheng Lin Qingyao Wu
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.