  Dealing with incomplete information is a well studied problem in the context
of machine learning and computational intelligence. However, in the context of
computer vision, the problem has only been studied in specific scenarios (e.g.,
certain types of occlusions in specific types of images), although it is common
to have incomplete information in visual data. This chapter describes the
design of an academic competition focusing on inpainting of images and video
sequences that was part of the competition program of WCCI2018 and had a
satellite event collocated with ECCV2018. The ChaLearn Looking at People
Inpainting Challenge aimed at advancing the state of the art on visual
inpainting by promoting the development of methods for recovering missing and
occluded information from images and video. Three tracks were proposed in which
visual inpainting might be helpful but still challenging: human body pose
estimation, text overlays removal and fingerprint denoising. This chapter
describes the design of the challenge, which includes the release of three
novel datasets, and the description of evaluation metrics, baselines and
evaluation protocol. The results of the challenge are analyzed and discussed in
detail and conclusions derived from this event are outlined.
