  The bokeh effect is an artistic technique that blurs out-of-focus areas in a
photograph and has gained interest due to recent developments in text-to-image
synthesis and the ubiquity of smart-phone cameras and photo-sharing apps. Prior
work on rendering bokeh effects have focused on post hoc image manipulation to
produce similar blurring effects in existing photographs using classical
computer graphics or neural rendering techniques, but have either depth
discontinuity artifacts or are restricted to reproducing bokeh effects that are
present in the training data. More recent diffusion based models can synthesize
images with an artistic style, but either require the generation of
high-dimensional masks, expensive fine-tuning, or affect global image
characteristics. In this paper, we present GBSD, the first generative
text-to-image model that synthesizes photorealistic images with a bokeh style.
Motivated by how image synthesis occurs progressively in diffusion models, our
approach combines latent diffusion models with a 2-stage conditioning algorithm
to render bokeh effects on semantically defined objects. Since we can focus the
effect on objects, this semantic bokeh effect is more versatile than classical
rendering techniques. We evaluate GBSD both quantitatively and qualitatively
and demonstrate its ability to be applied in both text-to-image and
image-to-image settings.
