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Posts
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publications
Projection-based classification of surfaces for 3D human mesh sequence retrieval
{"name"=>"Emery Pierson", "site"=>"https://daidedou.github.io/"}{"name"=>"Juan Carlos Alvarez Paiva", "site"=>"https://scholar.google.com/citations?user=OZJvCd8AAAAJ"}{"name"=>"Mohamed Daoudi", "site"=>"https://sites.google.com/view/mohameddaoudi"}, Computer & Graphics, Special Section Shape Modeling International, 2021
We analyze human poses and motion by introducing three sequences of easily calculated surface descriptors that are invariant under reparametrizations and Euclidean transformations. These descriptors are obtained by associating to each finitely-triangulated surface two functions on the unit sphere: for each unit vector u we compute the weighted area of the projection of the surface onto the plane orthogonal to u and the length of its projection onto the line spanned by u. The L2 norms and inner products of the projections of these functions onto the space of spherical harmonics of order k provide us with three sequences of Euclidean and reparametrization invariants of the surface. The use of these invariants reduces the comparison of 3D+time surface representations to the comparison of polygonal curves in Rn. The experimental results on the FAUST and CVSSP3D artificial datasets are promising. Moreover, a slight modification of our method yields good results on the noisy CVSSP3D real dataset.
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A Riemannian Framework for Analysis of Human Body Surface
{"name"=>"Emery Pierson", "site"=>"https://daidedou.github.io/"}{"name"=>"Mohamed Daoudi", "site"=>"https://sites.google.com/view/mohameddaoudi"}{"name"=>"Alice-Barbara Tumpach", "site"=>"https://math.univ-lille1.fr/~tumpach/Site/home.html"}, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022
We propose a novel framework for comparing 3D human shapes under the change of shape and pose. This problem is challenging since 3D human shapes vary significantly across subjects and body postures. We solve this problem by using a Riemannian approach. Our core contribution is the mapping of the human body surface to the space of metrics and normals. We equip this space with a family of Riemannian metrics, called Ebin (or DeWitt) metrics. We treat a human body surface as a point in a “shape space” equipped with a family of Riemmanian metrics. The family of metrics is invariant under rigid motions and reparametrizations; hence it induces a metric on the “shape space” of surfaces. Using the alignment of human bodies with a given template, we show that this family of metrics allows us to distinguish the changes in shape and pose. The proposed framework has several advantages. First, we define a family of metrics with desired invariant properties for the comparison of human shape. Second, we present an efficient framework to compute geodesic paths between human shape given the chosen metric. Third, this framework provides some basic tools for statistical shape analysis of human body surfaces. Finally, we demonstrate the utility of the proposed framework in pose and shape retrieval of human body.
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Parameterization Robustness of 3D Auto-Encoders
{"name"=>"Emery Pierson", "site"=>"https://daidedou.github.io/"}{"name"=>"Thomas Besnier", "site"=>"https://tbesnier.github.io/"}{"name"=>"Mohamed Daoudi", "site"=>"https://sites.google.com/view/mohameddaoudi"}{"name"=>"Sylvain Arguillère", "site"=>"http://math.univ-lyon1.fr/~arguillere/"}, Eurographics Workshop on 3D Object Retrieval (3DOR), 2022
The generation of 3-dimensional geometric objects in the most efficient way is a thriving research topic with, for example, the development of geometric deep learning, extending classical machine learning concepts to non euclidean data such as graphs or meshes. In this short paper, we study the effect of a reparameterization on two popular mesh and point cloud neural networks in an auto-encoder mode: PointNet [QSMG16] and SpiralNet [BBP∗19]. Finally, we tested a modified version of PointNet that takes orientation into account (through coordinates of the normals) as a first step towards the construction of a geometric deep learning model built with a more flexible metric regarding the parameterization. The experimental results on standardized face datasets show that SpiralNet is more robust to the reparametrization than PointNet in this specific context with the proposed reparameterization.
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3D Shape Sequence of Human Comparison and Classification Using Current and Varifolds
{"name"=>"Emery Pierson", "site"=>"https://daidedou.github.io/"}{"name"=>"Mohamed Daoudi", "site"=>"https://sites.google.com/view/mohameddaoudi"}{"name"=>"Sylvain Arguillère", "site"=>"http://math.univ-lyon1.fr/~arguillere/"}, European Conference on Computer Vision (ECCV) , 2022
Emery Pierson, Mohamed Daoudi, Sylvain Arguillere
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BaRe-ESA: A Riemannian Framework for Unregistered Human Body Shapes
{"name"=>"Emmanuel Hartmann", "site"=>"https://github.com/emmanuel-hartman/"}{"name"=>"Emery Pierson", "site"=>"https://daidedou.github.io/"}{"name"=>"Martin Bauer", "site"=>"https://www.math.fsu.edu/~bauer/"}{"name"=>"Nicolas Charon", "site"=>"https://www.math.uh.edu/~ncharon/index.html"}{"name"=>"Mohamed Daoudi", "site"=>"https://sites.google.com/view/mohameddaoudi"}, ICCV 2023, Paris, 2023
Emmanuel Hartman, Emery Pierson, Martin Bauer, Nicolas Charon, Mohamed Daoudi
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teaching
Introduction to basic mathematics
Undergraduate course, Université de Lille, 2021
Teaching of first years bachelors, and assistance for exercises. Design of midterm exams.
Computer Science Teaching assistant
Undergraduate course, Lille University Technological Institute (IUT de Lille) - Computer Science Department, 2022
Teaching assistant during lab sessions: Introduction to computer sience, object oriented programming, web design. Java, HTML, CSS.