Post-doctoral Research Visit F/m Postdoc in Data-driven Modelling and Simulation of Dense Crowds

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat**: CDD

**Niveau de diplôme exigé**: Thèse ou équivalent

**Fonction**: Post-Doctorant

**A propos du centre ou de la direction fonctionnelle**:
The Inria Centre at the University of Rennes is one of Inria’s nine centres and is home to more than thirty research teams. It is a major and well-recognized player in the field of digital science. The centre is at the heart of a rich ecosystem of R&D and innovation, involving highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education institutions, excellence laboratories, and a technological research institute.

**Contexte et atouts du poste**:
The Inria Centre at Rennes University is one of Inria's eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc. The VirtUs team has around 25 members, including 7 permanent researchers. The team is internationally recognized for its work in the fields of graphic animation and virtual reality. Crowd simulation and the study of collective human behavior is one of its major research themes.

**Mission confiée**:
**Context**:
This postdoctoral research focuses on the modelling and simulation of dense human crowds, i.e. crowds in which social and physical interactions between individuals are mixed. The study of the behaviour of dense crowds is an important issue in terms of crowd management and accident prevention. Simulation of dense crowds is an essential part of these studies, but modelling the physical interactions between individuals still poses many unexplored challenges. The proposed postdoctoral program aims to make significant progress in this direction, and more specifically, the research on dense crowds will spawn new classes of Artificial Intelligence models.

To explore this area, the VirtUs team has a unique set of field data that captures the behaviour of dense crowds as observed at music festivals. The dataset includes videos of the crowd at festivals, a few manually checked tracked trajectories of individuals in the crowd, as well as automatically tracked trajectories of a large number of people in the image. The dataset also includes full-body motion capture for equipped participants in the crowd. As a continuation of recent work on modelling dense crowds, this postdoc aims to exploit the existing dataset for modeling and simulation purposes.

**Objectives**

The main goal of the postdoc is to establish new approaches to crowd simulation that are capable of learning new crowd dynamics based on their video observation, and to establish new crowd analysis methods based on simulation to improve the understanding of dense crowd behaviours.

The main activity of the postdoc will be
- To explore new crowd data representations for machine learning approaches to crowd simulation
- To explore new models for data-driven crowd simulation
- To explore new metrics for the evaluation of crowd simulation and comparison with real-world data.

**Outcomes**

We will aim to publish the results of this research in the best conferences and journals in the field, including IEEE CVPR or ACM SIGGRAPH, ACM TOG.

**Bibliography**

Chatagnon, T., Olivier, A. H., Hoyet, L., Pettré, J., & Pontonnier, C. (2025). Classification of first recovery steps after quiet standing following external perturbation from different directions. _Journal of Biomechanics_, 112639.

van Toll, W., Braga, C., Solenthaler, B., & Pettré, J. (2020, October). Extreme-density crowd simulation: combining agents with smoothed particle hydrodynamics. In _Proceedings of the 13th ACM SIGGRAPH Conference on Motion, Interaction and Games_ (pp. 1-10).

Gomez-Nogales, G., Prieto-Martin, M., Romero, C., Comino-Trinidad, M., Ramon-Prieto, P., Olivier, A. H.,... & Casas, D. (2024). Resolving Collisions in Dense 3D Crowd Animations. _ACM Transactions on Graphics_, _43_(5), 1-14.

He, F., Yue, J., Zhu, J., Seyfried, A., Casas, D., Pettré, J., & Wang, H. (2025). Learning Extremely High Density Crowds as Active Matters. _arXiv preprint arXiv:2503.12168_.

Sundararaman, R., De Almeida Braga, C., Marchand, E., & Pettre, J. (2021). Tracking pedestrian heads in dense crowd. In _Proceedings of the IEEE/CVF conference on computer vision and pattern recognition_ (pp. 3865-3875).

Dang, H. T., Gaudou, B., & Verstaevel, N. (2024). A literature review of dense crowd simulation. _Simulation Modelling Practice and Theory_, 102955.

Van Toll, W., & Pettré, J. (2021, May). Algorithms for microscopic crowd simulation: Advancements in the 2010s. In _Computer Graphics Forum_ (Vol. 40, No. 2, pp. 731-754).

**Principales activités**:
Main activities (5 maximum):

- Pr