Aims and scope

Human-centered data are extremely widespread and have been intensely investigated by researchers belonging to even very different fields, including Computer Vision, Machine Learning, and Artificial Intelligence. These research efforts are motivated by the several highly-informative aspects of humans that can be investigated, ranging from corporal elements (e.g. bodies, faces, hands, anthropometric measurements) to emotions and outward appearance (e.g. human garments and accessories).

The huge amount and the extreme variety of this kind of data make the analysis and the use of learning approaches extremely challenging. In this context, several interesting problems can be addressed, such as the reliable detection and tracking of people, the estimation of the body pose, the development of new human-computer interaction paradigms based on expression and sentiment analysis.

Furthermore, considering the crucial impact of human-centered technologies in many industrial application domains, the demand for accurate models able also to run on mobile and embedded solutions is constantly increasing. For instance, the analysis and manipulation of garments and accessories worn by people can play a crucial role in the fashion business. Also, the human pose estimation can be used to monitor and guarantee the safety between workers and industrial robotic arms.

The goal of this workshop is to improve the communication between researchers and companies and to develop novel ideas that can shape the future of this area, in terms of motivations, methodologies, prospective trends, and potential industrial applications. Finally, a consideration about the privacy issues behind the acquisition and the use of human-centered data must be addressed for both the academia and companies.

Topics

We encourage submissions from all areas of computer vision, focusing on the analysis of humans. More general contributions such as novel theories, frameworks, architectures and datasets are also welcome. The topics of interest include but are not limited to:

3D Body and Face Reconstruction CHALLENGE

Image-based 3D human reconstruction has been widely applied in many fields such as VR/AR experience (e.g., movies, sports, games), video editing, and virtual avatars. Although related studies are developing rapidly, there are still many limitations and challenges. For example, the single-view-based 3d human reconstruction suffers from unsatisfactory artifacts on the back of human body. For 3D face reconstruction, mainstream methods adopt an orthogonal camera, which can not model the facial distortion caused by perspective projection.

This motivated the creation of MVP-Human (Multi-View and Multi-Pose 3D Human), a large-scale dataset, which contains hundreds of subjects, each of which contains multiple unconstrained frames and ground truth reconstruction meshes. We also collected the real 3D data of hundreds of ID faces based on standard perspective projection, including various expressions, head pose, and distance.

Based on our proposed MVP-Human dataset, we hold this challenge with two tracks to reconstruct the 3D human body and 3D face, respectively. The purpose of this competition is to provide a challenging benchmark to evaluate 3D human avatar reconstruction methods and facilitate researchers and developers to explore novel reconstruction approaches based on multi-view and multi-pose datasets. Welcome to join this competition and have fun!

CHALLENGE WEBPAGE