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3D virtual try-on enjoys many potential applications and hence has attractedwide attention. However, it remains a challenging task that has not beenadequately solved. Existing 2D virtual try-on methods cannot be directlyextended 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 topologicalstructure and with heavy computation. To deal with these problems, we propose aDecomposed Implicit garment transfer network (DI-Net), which can effortlesslyreconstruct a 3D human mesh with the newly try-on result and preserve thetexture from an arbitrary perspective. Specifically, DI-Net consists of twomodules: 1) A complementary warping module that warps the reference image tohave the same pose as the source image through dense correspondence learningand sparse flow learning; 2) A geometry-aware decomposed transfer module thatdecomposes the garment transfer into image layout based transfer and texturebased transfer, achieving surface and texture reconstruction by constructingpixel-aligned implicit functions. Experimental results show the effectivenessand superiority of our method in the 3D virtual try-on task, which can yieldmore high-quality results over other existing methods.

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