Hello, my name is Tor-Salve,
and I will present the paper “Modeling Pointing for 3D Target Selection in VR”
created together with Jarrod
Knibbe and Joanna Bergström.
In the real world,
humans interact only with objects that are in reach
due to the natural restrictions of arm's length.
In virtual reality, humans
also interact with objects within arm's reach,
but due to the freedom in virtual world, humans can use more or less
realistic transformations of the body to select objects.
For instance, the classical Go-Go technique
does elongate the virtual human
to select distant objects.
Another technique is Raycasting, where a potentially straight
line is originating from the human body,
intersecting these
targets.
But augmenting the body is not always desirable, and using great casting
techniques are not particularly accurate, as nomal human pointing isn't accurate.
With these technique,
humans need to adjust to the system
as they are in charge of correcting these natural errors,
and we know from previous work
that the pointing error is systematically inaccurate,
meaning that we can create models for correcting those errors.
Inspired by this by this previous work
we examine because free printing in order to devise a model
of how humans would naturally point at a 3D
target space.
The aim of our work is to analyze the movement data of humans to determine
set features, describing the movement as best as possible.
For this aim we collect movement data from bodies
where participants point at targets at a distance.
We then do feature selection on the collected data
to describe natural pointing movement.
After we estimate
the accuracy of the selected features using machine techniques.
To capture the movement data of humans pointing,
we invited participants to point targets in the space.
The 27 targets are spaced one meter apart,
and they are at a distance across a virtual.
With this, we got hold
of 1755 movement patterns
with these 27 targets.
From these data points we generated three scenarios
for each of which we generate a set of features.
In the first scenario,
we limit the data such that only the posture
describing the final pointing position is included.
This is motivated by the ease of implementation of a final selection
technique.
We generate a feature set consisting of
positional features such as position of the index finger
combined features such as the shoulder flexion.
Categorical features
such as an indicator of whether the hand is above the shoulder and distance
features, such as the distance between the index finger and the upper arm.
And most
oftentimes the targets in the center row in front of the participants
are classified wrong, suggesting that
pointing at these targets
that is very similar is similar across these targets.
In a second scenario, we included the full movement,
the motivation here is to be able to predict the target ahead of time
and to enable continuous target selection without the use of a trigger.
The feature set consists of the coefficients
of a third order polynomial fit on the index finger movement
and the velocity and acceleration
of the exact same index figure.
The prediction accuracy is inferior
to the previous scenario, although it is above chance level.
As the last scenario, we selected, a subset of the post features
consisting of features that could be tracked a simpler set up
than a full blown motion capturing system as we use it.
This could, for instance, be
hand-tracking built into an HMD or
external devices.
The feature set consists of data points on the head, wrist and hand
and has very similar prediction accuracy to that pose features,
meaning that a model using more accessible
tracking is equally good results as full-body tracking.
So what to do with all of this?
Well, we have shown that we can model human
movement with relatively simple features.
With this model, we can implement
its action techniques that are more natural than what we have
now, as we now can give cursor-free feedback
on a given selection target.
Further, we can expand this work by collecting
even more detailed data, such as, for instance, case
and all in all,
we can reliably determine a selection target based on human movement,
which allows for a wide range of applications in virtual reality.
Interesting as a next step
could, for instance, be to find out how well these
models translate into other realities,
namely augmented reality or the realities surrounding us
in everyday life, for instance, in a smart home setting.
But most importantly, using these models,
we redistribute the responsibility of adjusting to the system.
So now the system needs to adjust and not the human.
And with this, thanks for your time and see you around.