  Virtual try-on system under arbitrary human poses has huge application
potential, yet raises quite a lot of challenges, e.g. self-occlusions, heavy
misalignment among diverse poses, and diverse clothes textures. Existing
methods aim at fitting new clothes into a person can only transfer clothes on
the fixed human pose, but still show unsatisfactory performances which often
fail to preserve the identity, lose the texture details, and decrease the
diversity of poses. In this paper, we make the first attempt towards multi-pose
guided virtual try-on system, which enables transfer clothes on a person image
under diverse poses. Given an input person image, a desired clothes image, and
a desired pose, the proposed Multi-pose Guided Virtual Try-on Network (MG-VTON)
can generate a new person image after fitting the desired clothes into the
input image and manipulating human poses. Our MG-VTON is constructed in three
stages: 1) a desired human parsing map of the target image is synthesized to
match both the desired pose and the desired clothes shape; 2) a deep Warping
Generative Adversarial Network (Warp-GAN) warps the desired clothes appearance
into the synthesized human parsing map and alleviates the misalignment problem
between the input human pose and desired human pose; 3) a refinement render
utilizing multi-pose composition masks recovers the texture details of clothes
and removes some artifacts. Extensive experiments on well-known datasets and
our newly collected largest virtual try-on benchmark demonstrate that our
MG-VTON significantly outperforms all state-of-the-art methods both
qualitatively and quantitatively with promising multi-pose virtual try-on
performances.
