Presentation
Our TVCG paper on saliency-driven gaze animation is presented at SCA 2024!

Our IEEE TVCG paper “Real-time Multi-map Saliency-driven Gaze Behavior for Non-conversational Characters” is presented at ACM/Europgraphics Symposium on Computer Animation (SCA 2024) in Montreal, Canada. This paper introduces the first real-time fully data-driven gaze animation technique with unprecedent of level of realism. Still time to read our paper.
Symposium “Human Movement from the Origins to the Olympics”
SocNav Associate team

Today was the official launch of the SocNav Associate Team. The SocNav team is funded by Inria, promoting an interdisciplinary collaboration between Inria Centre de l’Université de Rennes (France) and Cirris – Centre interdisciplinaire de recherche en réadaptation et intégration sociale (Canada). More broadly, this project involves Cirris, LogVS team, VirtUs…
VirtUs is at the “Fête des Sciences 2022”, in Rennes

The VirtUs team is participating the “Fete des Sciences”, 2022 edition. We are performing live demonstrations in Virtual Reality of our immersive simulations. We also showcase some of our animation techniques, and show movies of recent experiments we performed. Please visit and join us if you are in Rennes.
UnderPressure? ACM SCA paper presentation
Papers at ACM MIG 2022
Paper accepted at Plos CB

Guess what happens when 2 groups of people cross? Stripes. In this paper recently accepted for publication to PlosCB, we explore the formation of stripes in crossing groups of people: stripe detection method, stripe features, dependence on crossing angle, etc. etc. Everything you’ve wanted to know about stripes but were…
Our “One-Man-Crowd” paper is among nominees for TVCG – Best Journal Paper award at IEEE VR 2022
Our paper “The One-Man-Crowd: Single User Generation of Crowd Motions Using Virtual Reality” (first author Tairan Yin) appeared in the list of nominees for the TVCG Best Paper Award at the IEEE VR 2022 conference, where it was presented. This paper introduces a brand new method for creating crowd datasets……