  We present a framework for automatically reconfiguring images of street
scenes by populating, depopulating, or repopulating them with objects such as
pedestrians or vehicles. Applications of this method include anonymizing images
to enhance privacy, generating data augmentations for perception tasks like
autonomous driving, and composing scenes to achieve a certain ambiance, such as
empty streets in the early morning. At a technical level, our work has three
primary contributions: (1) a method for clearing images of objects, (2) a
method for estimating sun direction from a single image, and (3) a way to
compose objects in scenes that respects scene geometry and illumination. Each
component is learned from data with minimal ground truth annotations, by making
creative use of large-numbers of short image bursts of street scenes. We
demonstrate convincing results on a range of street scenes and illustrate
potential applications.
