  Predicting where people can walk in a scene is important for many tasks,
including autonomous driving systems and human behavior analysis. Yet learning
a computational model for this purpose is challenging due to semantic ambiguity
and a lack of labeled data: current datasets only tell you where people are,
not where they could be. We tackle this problem by leveraging information from
existing datasets, without additional labeling. We first augment the set of
valid, labeled walkable regions by propagating person observations between
images, utilizing 3D information to create what we call hidden footprints.
However, this augmented data is still sparse. We devise a training strategy
designed for such sparse labels, combining a class-balanced classification loss
with a contextual adversarial loss. Using this strategy, we demonstrate a model
that learns to predict a walkability map from a single image. We evaluate our
model on the Waymo and Cityscapes datasets, demonstrating superior performance
compared to baselines and state-of-the-art models.
