ChaLearn Looking at People: Inpainting and Denoising challenges

TL;DR

This paper presents the design and outcomes of the ChaLearn Looking at People inpainting challenge, which aimed to advance visual inpainting techniques for images and videos across three challenging tasks.

Contribution

It introduces three novel datasets, evaluation metrics, and a comprehensive challenge framework to foster progress in visual inpainting for complex real-world scenarios.

Findings

Analysis of challenge results and participant methods

Improved inpainting techniques for human pose, text removal, and fingerprint denoising

Insights into future directions for visual inpainting research

Abstract

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.