Thursday, 25th of November 2021, 12:00 – 1:00

3D human pose estimation in the wild

Venue: 
online - link

Lecturer:
Stefano Fogarollo, Researcher at IGS

Abstract: 

Human pose estimation is a long-standing problem in the computer vision research area. The problem consists of retrieving the pose and the orientation of human body. Current standard pipelines assume to have training data in the form of images, camera information and 3D annotations. Such information can be only retrieved in a controlled settings and therefore current pipelines do not work well in the wild. The goal of the thesis is to conceive (and evaluate) a model that takes as input some images of the same human body and outputs its pose in the world coordinates. This model would be able to work in the wild where access to camera information is infeasible. With respect to the described goal, we successfully implemented and tested the method in the Human3.6M dataset.

 

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