MPACT: Mesoscopic Profiling and Abstraction of Crowd Trajectories

Simple item page
dc.contributor.authorLemonari, Marilenaen_US
dc.contributor.authorPanayiotou, Andreasen_US
dc.contributor.authorKyriakou, Theodorosen_US
dc.contributor.authorPelechano, Nuriaen_US
dc.contributor.authorChrysanthou, Yiorgosen_US
dc.contributor.authorAristidou, Andreasen_US
dc.contributor.authorCharalambous, Panayiotisen_US
dc.contributor.editorWimmer, Michaelen_US
dc.contributor.editorAlliez, Pierreen_US
dc.contributor.editorWestermann, Rüdigeren_US
dc.date.accessioned2025-11-07T08:33:14Z
dc.date.available2025-11-07T08:33:14Z
dc.date.issued2025
dc.description.abstractSimulating believable crowds for applications like movies or games is challenging due to the many components that comprise a realistic outcome. Users typically need to manually tune a large number of simulation parameters until they reach the desired results. We introduce MPACT, a framework that leverages image-based encoding to convert unlabelled crowd data into meaningful and controllable parameters for crowd generation. In essence, we train a parameter prediction network on a diverse set of synthetic data, which includes pairs of images and corresponding crowd profiles. The learned parameter space enables: (a) implicit crowd authoring and control, allowing users to define desired crowd scenarios using real-world trajectory data, and (b) crowd analysis, facilitating the identification of crowd behaviours in the input and the classification of unseen scenarios through operations within the latent space. We quantitatively and qualitatively evaluate our framework, comparing it against real-world data and selected baselines, while also conducting user studies with expert and novice users. Our experiments show that the generated crowds score high in terms of simulation believability, plausibility and crowd behaviour faithfulness.en_US
dc.description.number6
dc.description.sectionheadersOriginal Article
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70156
dc.identifier.issn1467-8659
dc.identifier.pages15 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70156
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70156
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectanimation
dc.subjectbehavioural animation
dc.subjectmotion control
dc.subjectmethods and applications
dc.subjectComputing methodologies → Motion path planning
dc.subjectIntelligent agents
dc.subjectReal-time simulation
dc.subjectNeural networks
dc.titleMPACT: Mesoscopic Profiling and Abstraction of Crowd Trajectoriesen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
10_cgf70156.pdf
Size:
5.36 MB
Format:
Adobe Portable Document Format
Download
Collections
44-Issue 6