We are developing computational models of autonomous virtual humans with applications in computer animation, intelligent virtual agents, and interactive narratives.
We develop models for simulating realistic, believable crowds. To this end, we identify and address fundamental limitations in how individuals in a crowd are represented and controlled. Our research results have widespread application in visual effects, games, urban planning, architecture design, as well as disaster and security simulation.
We are developing enhanced computer-aided design pipelines which empower architects, urban planners, and designers, to account for how human crowds inhabit and occupy functional spaces.
We develop computational tools to assist end users to create and experience compelling, interactive, digital stories. Our computational tools help mitigate the complexity of creating immersive digital stories without sacrificing any authorial precision.
Our research lies at the intersection of artificial intelligence, visual computing, and human-computer interaction with a mission to develop intelligent visual interfaces for human-guided content creation.
We have received funding NSF for our project, Learning Joint Crowd-Space Embeddings for Cross-Modal Crowd Behavior Prediction. Team: Vladimir Pavlovic (PI), Mubbasir Kapadia (Co-PI), Sejong Yoon (Co-PI) Project website: http://project-nucleum.github.io/ …
Our website for the project Human-Building Interaction is now live! The goal of this project is develop techniques to simulate and predict human behaviors in virtual and built-environments, and make …
Our paper, Laying the Foundations of Deep Long-Term Crowd Flow Prediction has been accepted for publication at the European Conference on Computer Vision (ECCV), 2020.
Our paper, Assessing crowd management strategies for the 2010 Love Parade disaster using computer simulations and virtual reality has been published in the Journal of the Royal Society Interface
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