Simulation And Animation Toolbox To Study Human Crowds In Extented Reality H/F

A propos d'Inria Inria, l'institut national de recherche dans les sciences et technologies du numérique, est en appui de l'État pour les stratégies nationales de recherche et d'innovation du numérique en tant qu'Agence de programmes. Inria mène plus de 300 projets de recherche et d'innovation avec ses 3500 scientifiques, ingénieurs et personnels d'appui, en partenariat avec les universités et l'écosystème numérique (entreprises, entrepreneurs, acteurs publics). Ensemble, nous explorons des domaines clés comme l'intelligence artificielle, la cybersécurité, l'informatique quantique, le Cloud, la transformation numérique de la santé, les jumeaux numériques ou encore les technologies numériques pour la défense. Nous construisons des solutions concrètes telles que des logiciels, des startups technologiques, des partenariats avec les entreprises du tissu national et des formations de pointe. Notre objectif : l'impact scientifique, technologique et industriel au service de la souveraineté numérique de la France.

A Simulation and Animation Toolbox to Study Human Crowds in eXtended Reality

Le descriptif de l'offre ci-dessous est en Anglais

Type de contrat : CDD

Niveau de diplôme exigé : Bac +5 ou équivalent

Fonction : Ingénieur scientifique contractuel

A propos du centre ou de la direction fonctionnelle

The Inria Centre at Rennes University is one of Inria's nine 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.

Contexte et atouts du poste

The VirtUs team at Inria has, over more than a decade, developed a recognised expertise in the simulation and animation of virtual crowds, combining agent-based crowd modelling, character animation, and perceptual evaluation to produce large-scale virtual populations that behave and move convincingly. This expertise is embodied in a set of software tools
- including the open-source crowd simulation engine Umans
- that are continuously extended and reused across the team's research projects, each of which brings its own requirements and use-cases.

Two projects currently illustrate this diversity. The ANR-FNR project PEAR investigates how realistic virtual crowds can be deployed on-site through Augmented Reality (AR), so that organisers of large-scale events
- music festivals, sporting ceremonies, urban celebrations
- can move beyond desk-based planning and experience crowd situations directly, from within a virtually populated version of their event site. The France 2030 project JUNN, on digital twins, relies on the same underlying crowd simulation capabilities to populate high-fidelity virtual replicas of real environments. To support these projects, the Virtus team has a need to consolidate some of its existing software, develop novel functionalities, and extend its scope of usability (e.g., real-time execution on AR hardware).

This engineer position aims to address these issues: consolidating the team's crowd simulation software into a coherent, maintainable and reusable toolbox, contributing to the development of novel functionalities to edit in real-time simulation trajectories to dedicated operational contexts, and contributing to porting these components to run in eXtended Reality (XR) to support use-cases developed in the team's current projects.

Mission confiée

Principales activités

Missions will include:

• Auditing and consolidating the team's existing crowd simulation and animation frameworks (simulation core, character animation, rendering)
  - improving its modularity, interoperability, documentation and test coverage, so that it can be reliably reused and extended across projects.
• Developing tools to edit and modify simulated trajectories and crowd data
  - e.g., reshaping simulated paths, adjusting local density or flow patterns, or directly editing individual trajectories
  - so that the populated crowds can be fine-tuned to match conditions observed in the field (from video recordings, sensor data, or other observations collected during real situations).
• Porting and optimising this consolidated framework to run at interactive frame rates on XR systems (in particular AR head-mounted displays), including dynamic quality adaptation mechanisms that adjust character appearance and animation quality to GPU and memory constraints on XR hardware.
• Defining and implementing clean