  Multi-pose virtual try-on (MPVTON) aims to fit a target garment onto a person
at a target pose. Compared to traditional virtual try-on (VTON) that fits the
garment but keeps the pose unchanged, MPVTON provides a better try-on
experience, but is also more challenging due to the dual garment and pose
editing objectives. Existing MPVTON methods adopt a pipeline comprising three
disjoint modules including a target semantic layout prediction module, a coarse
try-on image generator and a refinement try-on image generator. These models
are trained separately, leading to sub-optimal model training and
unsatisfactory results. In this paper, we propose a novel single stage model
for MPVTON. Key to our model is a parallel flow estimation module that predicts
the flow fields for both person and garment images conditioned on the target
pose. The predicted flows are subsequently used to warp the appearance feature
maps of the person and the garment images to construct a style map. The map is
then used to modulate the target pose's feature map for target try-on image
generation. With the parallel flow estimation design, our model can be trained
end-to-end in a single stage and is more computationally efficient, resulting
in new SOTA performance on existing MPVTON benchmarks. We further introduce
multi-task training and demonstrate that our model can also be applied for
traditional VTON and pose transfer tasks and achieve comparable performance to
SOTA specialized models on both tasks.
