University of Oulu

Application of 3D human pose estimation for motion capture and character animation

Saved in:
Author: Borodulina, Anastasiia1
Organizations: 1University of Oulu, Faculty of Information Technology and Electrical Engineering, Department of Computer Science and Engineering, Computer Science and Engineering
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.6 MB)
Pages: 58
Persistent link: http://urn.fi/URN:NBN:fi:oulu-201906262670
Language: English
Published: Oulu : A. Borodulina, 2019
Publish Date: 2019-06-26
Thesis type: Master's thesis
Tutor: Heikkilä, Janne
Reviewer: Heikkilä, Janne
Pedone, Matteo
Description:

Abstract

Interest in motion capture (mocap) technology is growing every day, and the number of possible applications is multiplying. But such systems are very expensive and are not affordable for personal use. Based on that, this thesis presents the framework that can produce mocap data from regular RGB video and then use it to animate a 3D character according to the movement of the person in the original video. To extract the mocap data from the input video, one of the three 3D pose estimation (PE) methods that are available within the scope of the project is used to determine where the joints of the person in each video frame are located in the 3D space. The 3D positions of the joints are used as mocap data and are imported to Blender which contains a simple 3D character. The data is assigned to the corresponding joints of the character to animate it. To test how the created animation will be working in a different environment, it was imported to the Unity game engine and applied to the native 3D character. The evaluation of the produced animations from Blender and Unity showed that even though the quality of the animation might be not perfect, the test subjects found this approach to animation promising. In addition, during the evaluation, a few issues were discovered and considered for future framework development.

see all

Subjects:
Copyright information: © Anastasiia Borodulina, 2019. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.