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 :
Analyse et reconnaissance de visages 2D et 3D dans leur environnement, identification de leur état et attributs
Ancrage sensorimoteur des interactions sociales non-verbales en réalité virtuelle
Reconnaissance faciale 2D/3D dans des conditions non contrôlées
Analyse de formes 4D humaine
Analysis/Prediction of Human Behavior Temporal Sequences in the Wild
Développement de nouveaux outils d'analyse des comportements, des émotions et du stress
Human object interaction recognition 09/01/2017
3D human action recognition 01/12/2015