Title: Can Adversarial Networks Hallucinate Occluded People With a Plausible Aspect?,
Abstract: When you see a person in a crowd, occluded by other persons, you miss visual
information that can be used to recognize, re-identify or simply classify him
or her. You can imagine its appearance given your experience, nothing more.
Similarly, AI solutions can try to hallucinate missing information with
specific deep learning architectures, suitably trained with people with and
without occlusions. The goal of this work is to generate a complete image of a
person, given an occluded version in input, that should be a) without occlusion
b) similar at pixel level to a completely visible people shape c) capable to
conserve similar visual attributes (e.g. male/female) of the original one. For
the purpose, we propose a new approach by integrating the state-of-the-art of
neural network architectures, namely U-nets and GANs, as well as discriminative
attribute classification nets, with an architecture specifically designed to
de-occlude people shapes. The network is trained to optimize a Loss function
which could take into account the aforementioned objectives. As well we propose
two datasets for testing our solution: the first one, occluded RAP, created
automatically by occluding real shapes of the RAP dataset (which collects also
attributes of the people aspect); the second is a large synthetic dataset, AiC,
generated in computer graphics with data extracted from the GTA video game,
that contains 3D data of occluded objects by construction. Results are
impressive and outperform any other previous proposal. This result could be an
initial step to many further researches to recognize people and their behavior
in an open crowded world.