3D SAM team

Modeling and Analysis of Static and Dynamic Shapes

Leader: Mohamed Daoudi



The general area of the 3D-SAM team will be to advance methodologies and algorithms for modeling and analyzing static and dynamic 3D shapes. Specifically, we will develop tools and methods for :

Shape analysis of surfaces: a large number of applications involve the statistical analysis and modeling of 3D objects that can be represented as surfaces in 3D. The difficulty in analyzing shapes of such objects comes from noisy and missing data, variability in pose and articulation, arbitrary mesh parametrizations during data collection, and shape variability within and across shape classes. These issues require metrics, representations and statistics that should have certain invariances and robustness to the aforementioned variabilities.

Modeling dynamics of shape variation in Videos: many human-machine interaction environments are characterized by temporally-evolving shapes of 3D objects. Examples include gesture and action recognition using full body scans, emotion classification using facial data and so on. It is critical to identify precise mathematical representations of underlying shapes, and then impose efficient dynamical models on representation spaces that capture the essential variability in shape evolution.

In the following we will describe some applications with high societal and economic impact such as biometric and security, health and retail sentiment analysis :

  • Applications to Human-Machine Interaction using 3D Sensing: we will target some specific application scenarios such as 3D human activities recognition to implement and evaluate the proposed techniques. An important application for future human-machine interaction is to develop estimation of human-state using 3D sensing.
  • Applications to 3D Face Dynamic Sequence Analysis: we will address the facial geometry analysis for recognition and facial expression recognition from 3D sequences (3D plus time, or 4D).
  • Applications to 3D Retrieval in Large Datasets: the use of 3D-model databases is growing both in number and size. To browse and search in such large and high-quality datasets, the 3D retrieval methods developed so far must be adapted and extended both in accuracy and speed.



On Availability

Amor Ben Tanfous

Un nouveau cadre géométrique en vue de l'interprétation de comportements humains à partir de formes 3D en mouvement

Oussema Bouafif

Analyse et reconnaissance de visages 2D et 3D dans leur environnement, identification de leur état et attributs

Nadia Hosni

Reconnaissance faciale 2D/3D dans des conditions non contrôlées

Benjamin Szczapa

Analysis/Prediction of Human Behavior Temporal Sequences in the Wild

Anis Kacem

Nouvelles approches géométriques pour l'analyse du comportement humain 2018-12-12

Quentin De Smedt

Dynamic hand gesture recognition from traditional handcrafted to recent deep learning approaches 2017-12-14

Meng Meng

Human object interaction recognition 2017-01-09

Vincent Leon

Semantic 3D model description – Application to example-based modeling 2016-11-10

Maxime Devanne

3D human action recognition 2015-12-01

Taleb Alashkar

3D Dynamic Facial Sequences Analysis For Face Recognition and Emotion Detection. 2015-11-02

Hazem Wannous

Towards Understanding Human Behavior by Time-Series Analysis of 3D Motion 2018-12-05

Other ' Image ' teams

FOX Imagerie Couleur