  The key challenge in designing a sketch representation lies with handling the
abstract and iconic nature of sketches. Existing work predominantly utilizes
either, (i) a pixelative format that treats sketches as natural images
employing off-the-shelf CNN-based networks, or (ii) an elaborately designed
vector format that leverages the structural information of drawing orders using
sequential RNN-based methods. While the pixelative format lacks intuitive
exploitation of structural cues, sketches in vector format are absent in most
cases limiting their practical usage. Hence, in this paper, we propose a
lattice structured sketch representation that not only removes the bottleneck
of requiring vector data but also preserves the structural cues that vector
data provides. Essentially, sketch lattice is a set of points sampled from the
pixelative format of the sketch using a lattice graph. We show that our lattice
structure is particularly amenable to structural changes that largely benefits
sketch abstraction modeling for generation tasks. Our lattice representation
could be effectively encoded using a graph model, that uses significantly fewer
model parameters (13.5 times lesser) than existing state-of-the-art. Extensive
experiments demonstrate the effectiveness of sketch lattice for sketch
manipulation, including sketch healing and image-to-sketch synthesis.
