My name is Roberto Montano, and I'm
going to present drift-correction techniques
for a scale-adapted VR navigation,
and my co-authors in this project
are Patricia Cornelio, Sriram Subramanian,
and Diego Martinez.
Before actually talk about what is this brief correction about,
I would like to explain a bit more
about scale-adapted techniques and how these techniques can
generate the drift.
Virtual reality allows compelling experiences.
Virtual reality navigation, when using a physical locomotion,
it's usually constrained to small spaces for navigation.
For instance, the green grid that is visible in the video
is representing the boundaries of the real space
available for navigation in the real world.
Scale-adapting techniques are expanding these boundaries,
allowing users to navigate larger areas
by dynamically scaling the use of displacement
along the navigation.
However, this dynamic use of translational gains
may induce an additional effect, which
is a mismatch between the real and virtual words.
And this is also called the drift effect.
This is an example of the drift effect
and how it is affecting [INAUDIBLE] navigation.
[MUSIC PLAYING]
I think that you have seen the similar examples on YouTube
about the constraints of these kind of things,
but it's clear from this example that we have a problem here.
So these kind of scale-adapted techniques
may induce these kind of things.
And at this point, we wanted to know
how bad these drift can become.
And in order to try to understand a bit more
about this, we carried out the user study
with 12 participants.
It took information about this effect in a navigation task.
We asked participants-- first, we
use six flags around [INAUDIBLE] environment,
and we asked participants to follow
semi-randomized sequences.
We used two pad lengths.
The first sequence following six flags
and the second part length following a 12-flag sequence.
We used a scale-adapted technique in the experiment.
And we used a scaled factor of 8 in the environment.
The result of the accumulated drift in doing the experiment
is shown here.
You can observe then a quick increment from the flag one
to the flag six of the drift and a kind of stabilization effect
from the flag seven.
It is still growing up but in a much slower progression.
It's good to mention that during the user study,
four participants couldn't finish the experiment,
because they were actually drifted out
of the tracking space.
And this causes that some of the flags
or the targets in the sequence were not reachable anymore.
So this is because of the drift effect.
And this is highlighting the detrimental effect of the drift
when using these kind of techniques for navigation.
Going back to the contributions for the paper,
we proposed a model to characterize
scale-adapted techniques, drift effect, and translational gain.
We modeled two different correction techniques,
and we also contributed to a user study that
shows the ability of our approach
to correct drift effect with two different scale-adapted
navigation techniques.
NaviFields that represent an example
of an exocentric control and seven league boots
that represents an example of an egocentric control.
This is the model that we propose in the paper.
It's a genetic model for drift correction technique that
is basically considering the position
of the user in the [INAUDIBLE] environment,
the use of displacement in the real world,
the scaling policy of the technique,
and of course, it's considering the correction function that
is going to be based in the approaches we are proposing
here.
The first one is a derivative correction
driven by the variation of the drifts at each point in time
during the navigation.
And the second approach is an angular correction
based on the implicit relationship between the drift,
the translational gain, and the user
displacement in the real world.
I'm not going to go into details about the models,
but I need to highlight the importance of two
variables in these models.
Epsilon that represents the balance of the correction,
so how much we are going to allow the correction
to influence the scaling policy in the technique,
and alpha that represents the aggressiveness
in the correction.
How quick do we want to the system
to respond to changes in drift?
The models were defined, but we still
need to find the proper or the best values
and the best configurations for these variables.
And to do this, we reuse the information
we collected in the first experiment about the navigation
of the 12 participants along the 12 flags,
and we implemented data-driven approach simulations
for this search.
And with S49 combination or possible combinations
of alpha and epsilon, and with the simulation,
we were trained to know or to investigate
the effect of these values over the correction techniques.
We can observe that the red color represents
the uncorrected techniques.
The purple represents the derivative approach,
and the blue one represents the angular.
After these simulations, we got that the best behavior
from our corrections is when the alpha and epsilon were 0.25.
In order to better analyze this data,
we generate bivariate mapping, considering
the standard deviation of the drift and the average drift.
The brighter color represents the better behavior
when correcting the techniques along the navigation.
We can observe that the results from the derivative approach
were not that good.
However, the result from the angular corrections
are quite promising.
And we can observe in the graph in the bottom
that the corrected version of using angular
is actually reducing the average drift from almost
70 centimeters to 20.
Once we have this data, we need to basically test
if the results from these simulations
will actually match with real participants in a navigation
task.
And we conducted a second user study
to test the behavior of the angular correction technique.
But we also wanted to know if our correction can
be generalized.
And we test our correction technique
with angular correction with two scale-adapted techniques.
NaviFields field as a representative
of an egocentric control, where the scaling depends exclusively
on the location of the user in the virtual environment
or the relevance of each location
in the environment and seven league boots
that represents an egocentric control, where
the scaling depends exclusively on the user speed.
On the right, you can see the environment
we use is pretty similar to the one we used in the first study.
However, we add an additional area
in the center of the environment,
and we adjust some parameters in order
to have a fair comparison between these two techniques.
Also, instead of testing pad lengths of six flags and 12,
in this case, we extend this length
in order to really challenge the user
and see how much this drift can grow over time.
In this case, we are using 42 sequences of flags--
pad length of 42, sorry.
But in order to know how to guide
our exploration in this user study,
we wanted to answer some questions.
Of course, one of the important ones
is if our correction is working.
And to do that, we are measuring the drift at the end
of each seventh flag pattern.
If you remember from the first user study,
there was an increment in drift along the first six flags.
So we wanted to measure this drift
when this theoretical increment in drift is maximum.
The second question is if our correction technique
is actually extending the navigation.
One of the problems we have in the first user study
is that some of the participants were drifted out
of the tracking space.
They couldn't actually finish the sequence of the targets
in the navigation.
So in this case, we wanted to know,
we are considering the number of flags
that actually the participants can
reach when navigating with the corrected
version of the technique and versus the uncorrected one.
And finally, we wanted to know if this correction technique is
negatively affecting the scaling adaptive technique
performance of behavior.
To do that, we are measuring distance per flag,
time per flag, and user's perceptions
in order to compare.
This user perception is measured using a cautionary focus
on comfort, naturalness, consistency,
and we're going to discuss a bit more about that later.
This is an example of the correction technique
in our user study.
In this case, this is focused on NaviFields,
the uncorrected version on top, the corrected
version on the bottom.
[MUSIC PLAYING]
Similarly, the correction made using seven league boots.
The results of this user study highlight the fact
that our correction method is actually
reducing the drift effect of comparing
versus the uncorrected technique.
As we can see in the graph, we have some data missing there.
That's because participants would naturally
reach these flags.
For example, the flat 28, 35, and 42.
And when compared the mean correction in terms of drift,
we found significant difference between the drift
that was accumulated when using our corrected
version of the technique and when they were
using the uncorrected version.
Our correction method allows extended navigation.
As you can see in the graph, we found a significant difference
in the number of the flags that the participants
could reach when using the corrected
version of the techniques.
But this correction was not affecting the influence--
did not negatively influence the performance
of the original technique, as we couldn't
find significant difference in terms of distance
per flag and time for flag.
Our correction method did not negatively
influence the experience when comparing
the original technique against the corrected counterpart.
And that said, the curious thing because
in the example in the top, we found
no significant difference.
And we were expecting that.
But something happened when using seven league boots.
Maybe we think that there was a kind
of self-achievement effect.
So as the users were reaching more higher number of flags
or extending in navigation time, they
could attribute some of this to some
of the factors in the [INAUDIBLE]..
Summary.
We explore and demonstrate the detrimental effect of drift,
that the drift produces only scale VR navigation techniques.
We propose a model to characterize
scale-adapted techniques.
And we model two different correction techniques,
derivative and angular.
And we tune our correction techniques using simulations
and found that angular correction performed better
than derivative correction.
Through our user study, we showed
that the angular correction was able to significantly reduce
the drift effect into different scale-adapted techniques.
NaviFields as an example of an exocentric control and seven
league boots as an example of egocentric control.
And finally, our correction technique
allowed standard navigation without affecting
the performance of the original technique while not hindering
a user's experience.
That's all for my talk.
Thank you very much.
[APPLAUSE]
Wonderful.
We have time for questions.
Any questions from the audience?
And thanks for bearing within.
Great job talking well during the construction.
All right.
Well, while you're all thinking about your questions,
so I really think your results are
quite impressive and overall a really exciting paper.
I'm wondering if you could talk a little bit more
about your optimization and sort of the terms
that you're trying to minimize.
And so obviously drift is a big one that's easy to model.
And then there's other things, like how people
are going to feel about it.
And are they going to notice this redirection or movement
or is it going to make them more nauseous or other things?
How do you think we start to build that
into the optimization?
Yeah.
First of all, during the optimization process,
we need to consider some factors.
In this case, one thing I didn't mention here
is that in order to measure the drift,
the drift is in a special kind of transactional game
that can only be measured in closed pads.
So in order to actually measure or estimate the drift,
we had to sort first simulate the path, the way back
from each point in the navigation to the center.
Then we can estimate the drift.
And based on that--
sorry.
Can you repeat the question again?
Sure.
Just in terms of thinking about what are the terms that you're
currently minimizing for and then what
are things that are outside of our ability
to sort of have a way to model them
and find cost function for them right now?
In this case, the only factor that we were minimizing
was a focus on the drift.
And do you think about, you know,
what are some of the other factors that you might want
to optimize for if you could?
In this case, we can also consider time,
the distance of the trajectory.
So we can actually--
if we can reduce the trajectory, we
can also kind of reduce the speed or the effect
of the motion sickness.
Great.
Any questions from the audience?
All right.
Well, I guess I'm wondering, you know,
in terms of other techniques that you
think this could be applied to, you
sort of showed the seven leagues boots
and some of these other scale-adaptive techniques.
Do you think, you know, we could put this
into some of the other types of locomotion techniques?
Yeah.
Actually, not only in locomotion techniques.
We can actually have a good complement, for instance,
with some teleporting approaches.
So the thing is that this technique would complement,
for instance, the [INAUDIBLE] teleporting,
because at the end, we are trying to reduce the drift.
And the drift is a misalignment between the center
of the wheel and [INAUDIBLE].
If we can combine this approach, which is reducing the drift,
and combine it with that teleporting, projected
teleporting, we can actually disappear the drift
in periods of time.
great all right.
Well, let's think the speaker.
[APPLAUSE]