  Lighting effects such as shadows or reflections are key in making synthetic
images realistic and visually appealing. To generate such effects, traditional
computer graphics uses a physically-based renderer along with 3D geometry. To
compensate for the lack of geometry in 2D Image compositing, recent deep
learning-based approaches introduced a pixel height representation to generate
soft shadows and reflections. However, the lack of geometry limits the quality
of the generated soft shadows and constrain reflections to pure specular ones.
We introduce PixHt-Lab, a system leveraging an explicit mapping from pixel
height representation to 3D space. Using this mapping, PixHt-Lab reconstructs
both the cutout and background geometry and renders realistic, diverse,
lighting effects for image compositing. Given a surface with physically-based
materials, we can render reflections with varying glossiness. To generate more
realistic soft shadows, we further propose to use 3D-aware buffer channels to
guide a neural renderer. Both quantitative and qualitative evaluations
demonstrate that PixHt-Lab significantly improves soft shadow generation.
