Skeleton-Based Motion Capture for Robust Reconstruction of Human Motion
CA '00 Proceedings of the Computer Animation
Performance animation from low-dimensional control signals
ACM SIGGRAPH 2005 Papers
New Insights into Pedestrian Flow Through Bottlenecks
Transportation Science
Experiment-based modeling, simulation and validation of interactions between virtual walkers
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Conditional stochastic simulation for character animation
Computer Animation and Virtual Worlds - CASA' 2010 Special Issue
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Realistic following behaviors for crowd simulation
Computer Graphics Forum
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Reconstruction is a key step of the motion capture process. The quality of motion data first results from the quality of raw data. However, it also depends on the motion reconstruction step, especially when raw data suffer markers losses or noise due, for example, to challenging conditions of capture. Labeling is a final and crucial data reconstruction step that enables practical use of motion data (e.g., analysis). The lower the data quality, the more time consuming and tedious the labeling step, because human intervention cannot be avoided: he has to manually indicate markers label each time a loss of the marker in time occurs. In the context of crowd study, we faced such situation when we performed experiments on the locomotion of groups of people. Data reconstruction poses several problems such as markers labeling, interpolation and mean position computation. While Vicon IQ software has difficulties to automatically label markers for the crowd experiment we carried out, we propose a specific method to label our data and estimate participants mean positions with incomplete data.