Hi, I'm Daniele and I'm glad to be here to present our study entitled "Perceived Realism
of Pedestrian Crowds Trajectories in VR" This project is the result of the joint collaboration
between the University College London, Imperial College, CYENS and
the Istituto Italiano di Tecnologia.
VR games such as Emergence or Humanity focus on user experience in crowded environments.
At the same time, VR serious games such as evacuation training have gained a lot of interest
in recent years. In these experiences, producing plausible
trajectories can be a high computational cost for real-time graphics engines, especially
for standalone VR devices. We explore how real users perceived computationally
inexpensive rectilinear trajectories in an immersive environment.
To do this, we simulated a large crowd and compared the perceived realism of synthetic
trajectories and trajectories extracted from real data.
We then measured the impact that the plausibility of the trajectories had on the perceived immersion
of users.
Crowds simulations in VR have been studied extensively in the last decade.
In particulat the Realism of Crowds has been evaluated measuring the level of presence
achieved by a human. VR has been used as an experimental tool to
improve the level of realism of the microscopic pedestrian simulation.
For example to assess local movements (such as collision avoidance), and trajectories
formed by the participants against real situations. How to Measure Realism of crowd trajectories
is a different topic. There are several approaches to measure the
similarity between simulated data and real-world data.
For example, by defining an entropy metric that compares entire simulations to reference
data. It is possible to apply other laws from different
research areas such as the principle of Pareto optimality
to compare between crowd data under multiple (often conflicting) criteria, or defining
an inference-based similarity metric.
One important aspect when evaluating realism is the point of view and the immersion level
Since immersion is a topic highly correlated to the observation point, we consider these
studies highly informative. In addition Different aspects may affects
the realism/authenticity of a crowd: graphical fidelity, avatar realism,
social-forces, social gestures and potentially more.
But such simulations to be run in a standalone device needs to be cheap. Previous works suggests
that very simple crowd behaviours, such as ones having rectilinear and single
speed trajectories can be perceived as plausible while being computationally inexpensive
In the same context, by using camera angle as an experimental condition we can evaluate
crowd plausibility, suggesting that aerial views increase the
perceived effect of pedestrian manoeuvring illusion.
We know that in general trajectory realism is affected by motion illusion effect so we
aim to understand if it can be perceived even in this constrained
urban condition where pedestrians are moving in opposite directions.
We address the challenge of understanding if computationally inexpensive algorithms
can generate realistic trajectories in a scenario showing pedestrians
following urban constraints such as a road crossing.
And this is done by measuring it from experiment.
Our approach consist in: - generating Real trajectory from real case
video - generating synthetic simulations that were
rendered from different camera angles. adopting the rectilinear algorithm to simulate
a pedestrian crossing, generating the trajectories of each of the simulated agents.
-measuring how rectilinear trajectories perform in a two-dimensional display evaluating the
most realistic point of observation, in search of motion illusion.
- With such an algorithm we repeat the experiment comparing the trajectories realism with real
data in VR.
The real trajectories came from a commercial footage of a crowded pedestrian crossing containing
280 pedestrians over 30 seconds. Trajectories were extracted by tracking pedestrians'
heads with an open-source computer vision software library.
By measuring different features on the video, we estimated the size of the scene as 20x40
in meters.
The synthetic simulations consisted of constant velocity, rectilinear, collision-free paths.
The entry and exit positions alongside the time that pedestrians appeared in the real-world
video were used to initialize starting areas, goal areas and preferred velocities for each
simulated character. For the preferred velocities, in particular,
a Monte Carlo approach was used; preferred speeds were sampled from the
speed distribution of the actual pedestrians.
We want to keep a realistic animation for all our tests.
We took our inspiration from the locomotion synthesis work of Holden in 2017 based on
Deep Learning. We took the Microsoft RocketBox character
set and applied animations to them. We recorded the joint positions to avoid performing the
simulation in real-time. This was required for performing the experiment
on untethered devices such as the Oculus Quest.
We designed two user studies. In the first 153 participants performed the
experiment remotely. We shows two videos of rectilinear trajectories crowd simulations
from different camera angle (front view, aerial view)
And ask to evaluate the realism via a questionnaire.
The second experiment was performed in VR and measured how users perceived the realism
of the rectilinear trajectories compared with the real trajectories. We also
aimed to understand how much trajectory realism impacted the
level of immersion experienced by participants.
Both experiments has a similar design. While the first experiment highlights how synthetic
simulations are affected by the point of view, and motion illusion was present,
we selected the most realistic POV for the second experiment to compare with the immersive
scenario. In VR we compare trajectories coming from real data to synthetic ones.
In the first experiment, 75% reported perceiving the rectilinear algorithm as either realistic
or plausible. The results validated the plausibility of
the rectilinear trajectories especially in the aerial view, that we select as
term of comparison in the second experiment based in VR.
For the second experiment, We developed an application within unity, targeting Oculus
Quest 1 or 2. The participant were asked to evaluate the
realism of synthetic and real data in an immersive scenario or an aerial view displayed on
a screen in a VR environment. Perceived realism was again recorded via the
same questions of the first experiment. Results showed no statistical difference between
the perceived realism of linear trajectories and real trajectories.
Also results showed no statistical difference for the immersion level experienced between
real and rectilinear trajectories.
To summarize our study: we identify and validate through a user study this computationally
inexpensive method of rectilinear trajectories. Validation consists of measuring perceived
realism to assess the generation of a plausible set of trajectories for a high-density
pedestrian crossing scene. Secondly, We compared via a VR user test the
perception of realism between the simulated trajectories and a set of real-
world trajectories traced from video footage. These experiment outcomes highlight how spending
computational power for trajectory realism does not necessarily
increase the levels of perceived realism and experienced immersion in a high-density crowd
crosswalk setting
Thank you for your attention, and I’m happy to answer your questions.