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Deep Learning for Three-dimensional (3D) Humans
Lecturer
Mohamed Daoudi, mohamed.daoudi@imt-nord-europe.fr
Juan-Carlos Alvarez-Paiva, University of Lille,
Naima Otberdout, University of Lille,
Emery Pierson, University of Lille,
Organizer/s
University of Lille / IMT Lille Douai / CRIStAL (UMR CNRS 9189)
Content and organization
The success of deep learning in computer vision and image analysis, speech recognition, and natural language processing has driven the recent interest in developing similar models for 3D geometric data. However, it is less obvious how using convolutional neural networks (CNNs) architectures can be adapted to 3D data, given in the form of point clouds or meshes, where a regular structure is not directly available. The purpose of this course is to overview the foundations and the current state of the art in deep learning techniques for 3D shape analysis. This short course will cover the following topics:
– Fundamentals of differential geometry of surfaces.
– Classical methods for 3D shape analysis.
– Deep learning for 3D data: basic concepts of deep learning; extending CNN to 3D data;
– Generative methods for 3D data, autoencoders and GAN methods for 3D data.
The targeted applications will be in 3D face and body shape analysis, and human behavior understanding.
Course Duration
09:00 to 17.00 CET
Course Type
Short Course
Participation terms
Registration is free of charge.
If you are an AIDA Student* already, please:
Step (a) register in the course by filling the following form https://forms.gle/NW6sW4DacqsTm5XeA
AND
Step (b) enroll in the same course in the AIDA system (Deep Learning for Three-dimensional (3D) Humans), so that this course enter your AIDA Course Attendance Certificate.
If you are not an AIDA Student do only step (a).
*AIDA Students should have been registered in the AIDA system already (they are PhD students or PostDocs that belong only to the AIDA Members listed in this page: Members)
Schedule
January 17th 2022
Language
English
Modality (online/in person):
Online
