  We propose BokehMe, a hybrid bokeh rendering framework that marries a neural
renderer with a classical physically motivated renderer. Given a single image
and a potentially imperfect disparity map, BokehMe generates high-resolution
photo-realistic bokeh effects with adjustable blur size, focal plane, and
aperture shape. To this end, we analyze the errors from the classical
scattering-based method and derive a formulation to calculate an error map.
Based on this formulation, we implement the classical renderer by a
scattering-based method and propose a two-stage neural renderer to fix the
erroneous areas from the classical renderer. The neural renderer employs a
dynamic multi-scale scheme to efficiently handle arbitrary blur sizes, and it
is trained to handle imperfect disparity input. Experiments show that our
method compares favorably against previous methods on both synthetic image data
and real image data with predicted disparity. A user study is further conducted
to validate the advantage of our method.
