  We present CoGS, a novel method for the style-conditioned, sketch-driven
synthesis of images. CoGS enables exploration of diverse appearance
possibilities for a given sketched object, enabling decoupled control over the
structure and the appearance of the output. Coarse-grained control over object
structure and appearance are enabled via an input sketch and an exemplar
"style" conditioning image to a transformer-based sketch and style encoder to
generate a discrete codebook representation. We map the codebook representation
into a metric space, enabling fine-grained control over selection and
interpolation between multiple synthesis options before generating the image
via a vector quantized GAN (VQGAN) decoder. Our framework thereby unifies
search and synthesis tasks, in that a sketch and style pair may be used to run
an initial synthesis which may be refined via combination with similar results
in a search corpus to produce an image more closely matching the user's intent.
We show that our model, trained on the 125 object classes of our newly created
Pseudosketches dataset, is capable of producing a diverse gamut of semantic
content and appearance styles.
