  The fast growth of E-Commerce creates a global market worth USD 821 billion
for online fashion shopping. What unique about fashion presentation is that,
the same design can usually be offered with different cloths textures. However,
only real video capturing or manual per-frame editing can be used for virtual
showcase on the same design with different textures, both of which are heavily
labor intensive. In this paper, we present a pattern-based approach for UV and
shading recovery from a captured real video so that the garment's texture can
be replaced automatically. The core of our approach is a per-pixel UV
regression module via blended-weight multilayer perceptrons (MLPs) driven by
the detected discrete correspondences from the cloth pattern. We propose a
novel loss on the Jacobian of the UV mapping to create pleasant seams around
the folding areas and the boundary of occluded regions while avoiding UV
distortion. We also adopts the temporal constraint to ensure consistency and
accuracy in UV prediction across adjacent frames. We show that our approach is
robust to a variety type of clothes, in the wild illuminations and with
challenging motions. We show plausible texture replacement results in our
experiment, in which the folding and overlapping of the garment can be greatly
preserved. We also show clear qualitative and quantitative improvement compared
to the baselines as well. With the one-click setup, we look forward to our
approach contributing to the growth of fashion E-commerce.
