Dear Fellow Scholars, this is Two Minute Papers
with Károly Zsolnai-Fehér.
If we have an animation movie or a computer
game with quadrupeds, and we are yearning
for really high-quality, lifelike animations,
motion capture is often the go-to tool for
that.
Motion capture means that we put an actor,
in our case, a dog in the studio, we ask it
to perform sitting, trotting, pacing and jumping,
record its motion, and transfer it onto our
virtual character.
This generally works quite well, however,
there are many difficulties with this process.
We will skip over the fact that an artist
or engineer has to clean and label the recorded
data, which is quite labor-intensive, but
there is a bigger problem.
We have all these individual motion types
at our disposal, however, a virtual character
will also need to be able to transition between
these motions in a smooth and natural manner.
Recording all possible transitions between
these moves is not feasible, so in an earlier
work, we looked at a neural network-based
technique to try to weave these motions together.
For the first sight, this looks great, however,
have a look at these weird sliding motions
that it produces.
Do you see them?
They look quite unnatural.
This new method tries to address this problem
but ends up offering much, much more than
that.
It requires only 1 hour of motion capture
data, and we have only around 30 seconds of
footage of jumping motions, which is basically
next to nothing.
And this technique can deal with unstructured
data, meaning that it doesn't require manual
labeling of the individual motion types, which
saves a ton of work hours.
Beyond that, as we control this character
in the game, this technique also uses a prediction
network to guess the next motion type, and
a gating network that helps blending together
these different motion types.
Both of these units are neural networks.
On the right, you see the results with the
new method compared to a standard neural-network
based solution on the left.
Make sure to pay special attention to the
foot sliding issues with the solution on the
left and note that the new method doesn't
produce any of those.
Now, these motions look great, but they all
take place on a flat surface.
You see here that this new technique excels
at much more challenging landscapes as well.
This technique is a total powerhouse, and
I can only imagine how many work hours this
will save for artists working in the industry.
It is also scientifically interesting and
quite practical.
My favorite combination.
It is also well evaluated, so make sure to
have a look at the paper for more details.
Thanks for watching and for your generous
support, and I'll see you next time!
