  Shadows are essential for realistic image compositing. Physics-based shadow
rendering methods require 3D geometries, which are not always available. Deep
learning-based shadow synthesis methods learn a mapping from the light
information to an object's shadow without explicitly modeling the shadow
geometry. Still, they lack control and are prone to visual artifacts. We
introduce pixel heigh, a novel geometry representation that encodes the
correlations between objects, ground, and camera pose. The pixel height can be
calculated from 3D geometries, manually annotated on 2D images, and can also be
predicted from a single-view RGB image by a supervised approach. It can be used
to calculate hard shadows in a 2D image based on the projective geometry,
providing precise control of the shadows' direction and shape. Furthermore, we
propose a data-driven soft shadow generator to apply softness to a hard shadow
based on a softness input parameter. Qualitative and quantitative evaluations
demonstrate that the proposed pixel height significantly improves the quality
of the shadow generation while allowing for controllability.
