Exploiting Motion Capture to Enhance Avoidance Behaviour in Games


Realistic simulation of interacting virtual characters is essential in computer games, training and simulation applications. The problem is very challenging since people are accustomed to real-world situations and thus, they can immediately detect inconsistencies and artifacts in the simulations. Over the past twenty years several models have been proposed for simulating individuals, groups and crowds of characters. However, little effort has been made to actually understand how humans solve interactions and avoid inter-collisions in real-life. In this paper, we exploit motion capture data to gain more insights into human-human interactions. We propose four measures to describe the collision-avoidance behavior. Based on these measures, we extract simple rules that can be applied on top of existing agent and force based approaches, increasing the realism of the resulting simulations.

Experimental Setup

9 female and 13 male participants gave informed consent to participate in our experiment. From the 22 participants, 18 pairs were selected based on gender (male-male, female-male, female-female). The size of the participant (short or tall) was also annotated. Cut off length for males was at 170 cm and 160 cm for females. For each pair, a trial consists of both participants walking between two points marked on the floor. The participants start at the same time and walk in opposite directions, having to avoid each other somewhere along the path. To shift their attention from the task, the participants were provided with a cognitive workload task. During every trial, they needed to memorize a number, printed on a note at the start point. This number had to be written down at the end point.

Experimental Dataset

The experimental interactions data can be found in the following zip file Dataset.zip. The file consists of three directories. Each directory is organized based on the gender of the participants. All data are in BVH format. A simple BVH parser is also included in the zip file.


Exploiting Motion Capture to Enhance Avoidance Behaviour in Games,
B.J.H van Basten, S.E.M. Jansen, and I. Karamouzas
The Second International Workshop on Motion in Games (MIG 2009)
[pdf] [bib] [Dataset]

This research has been supported by the GATE project, funded by the Netherlands Organization for Scientific Research (NWO) and the Netherlands ICT Research and Innovation Authority (ICT Regie).

The dataset is freely available and can be used for any purpose. We would appreciate, though, if you could mention the GATE project or cite our related publication.