  Modelling human free-hand sketches has become topical recently, driven by
practical applications such as fine-grained sketch based image retrieval
(FG-SBIR). Sketches are clearly related to photo edge-maps, but a human
free-hand sketch of a photo is not simply a clean rendering of that photo's
edge map. Instead there is a fundamental process of abstraction and iconic
rendering, where overall geometry is warped and salient details are selectively
included. In this paper we study this sketching process and attempt to invert
it. We model this inversion by translating iconic free-hand sketches to
contours that resemble more geometrically realistic projections of object
boundaries, and separately factorise out the salient added details. This
factorised re-representation makes it easier to match a free-hand sketch to a
photo instance of an object. Specifically, we propose a novel unsupervised
image style transfer model based on enforcing a cyclic embedding consistency
constraint. A deep FG-SBIR model is then formulated to accommodate
complementary discriminative detail from each factorised sketch for better
matching with the corresponding photo. Our method is evaluated both
qualitatively and quantitatively to demonstrate its superiority over a number
of state-of-the-art alternatives for style transfer and FG-SBIR.
