Julien Pettre, Inria Research Scientist

Virtus team (https://team.inria.fr/virtus/)
tel: +33 (0) 2 99 84 22 36
Inria-Rennes, Campus de Beaulieu
35042 Rennes cedex, FRANCE

Julien Pettre Homepage

Short Bio

Julien Pettré is a computer scientist. He is senior researcher at Inria, the French National Institute for Research in Computer Science and Control. He is leading the VirtUs team at the Inria Center of Rennes. He received PhD from the University of Toulouse III in 2003, and Habilitation from the University of Rennes I in 2015. From 2004 to 2006, he was postdoctoral fellow at EPFL in Switzerland. He joined Inria in 2006.
Julien Pettré is coordinator of the European H2020 Fet Open CrowdDNA project (2020-2024), dedicated to future emergent technologies for crowd management in public spaces. He previously coordinated the European H2020 Crowdbot project (2018-21) dedicated to the design of robot navigation techniques for crowded environments, as well as the national ANR JCJC Percolation project (2013-17) dedicated to the design of new microscopic crowd simulation algorithms, and the national ANR CONTINT Chrome project, dedicated to efficient and designer-friendly techniques for crowd animation.
His research interests are crowd modeling and simulation, computer animation, virtual reality, robot navigation and motion planning.

Research Topics

Crowd Simulation

Crowd Simulation

A crowd is a gathering of many people le in a same place. The purpose of crowd simulation research is to create computer algorithms which compute the motion of a crowd, in order to understand, reproduce or predict its behavior. It is generally accepted that crowd motion is an emergent phenomenon, which means that the global crowd motion result from all the local interactions between individuals.
Microscopic crowd simulation algorithms compute the motion of virtual crowds in the same way: they are based on a set of numerical models of local interactions. We have proposed several new models of local interactions. We especially considered collision avoidance and following interactions. We pioneered the "velocity-based" models of collision avoidance, which enable agents to perform adaptations with anticipation, which better reflects real human behaviors.

Crowd Studies

The design of realistic microscopic crowd simulation algorithms requires extending knowledge on individual behaviors in crowds and their repercussion at the collective scales. We have performed a number of experiments to decipher such behaviors. Through several experimental campaigns, we studied collision avoidance behaviors, following behaviors, unidirectionnal and bidirectionnal flows of people, crossing flows of people, etc. Recently, the Inria media team made a video footage about one of our experimental campaign. You will have more explanations about our experimental approach, and their links with our modelling and simulation activities.

Crowd Animation Design

Crowd Animation

Creating crowd animations can be a tedious task for designers. They have to play on the numerous parameters of crowd simulators to generate new animations, we can lead to few trial We explore new animation techniques, such as the crowd patches approach. In contrast with the use of simulators, in this approach, designers directly manipulate assemblies of crowd patches (pre-computed elements of crowd animations), with direct visualization of results, resulting in more intuitive techniques.

Real-time Crowds

Real-time Crowds

The display of interactive virtual crowds, such as for video games or virtual reality applications requires efficient techniques to simulate and visualize crowds. To match this requirement for performances, we conduct perception studies to focus the available computation resources on simulation details which count most for human spectators.
For example, we could prove the importance of inserting full body motion details in crowd characters motion on the perceived realism of a crowd animation.

Virtual Reality Crowds

Virtual Reality Crowds

Virtual reality allows us to set real humans in interaction with their digital counterparts. Doing so, we can study more detailed situations of local interactions, perfectly control our experimental situations, and more easily acquire experimental data.
We are currently developing this research direction to answer scientific questions on human behaviors in crowds, which would be out of reach without such technology.
To know more, check our papers below!

Major publications (see them all)


PhD Students


Research Sponsors

ANR Crowd Animation

Want to know more?

You may read our papers above, or check our Youtube channel to see all our paper submission videos. Below are few samples: