Deep Learning for Three-dimensional (3D) Humans

Starts on 12/01/2021

Ends on 12/31/2021

Deep Learning for Three-dimensional (3D) Humans

Mohamed Daoudi,


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

4 hours

Course Type

short Course


End 2021



Modality (online/in person):

Preferably physical but web also possible

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