IBC
New Pixar
animation Elemental is the Walt Disney company’s most technically complex
feature film to date and required a new data storage pipe that lays the
foundation for use of AI.
article here
“We are not
actively using AI yet, but we have laid the foundation,” began Eric Bermender,
Head of Data Center and IT Infrastructure at Pixar Animation Studios.
“One thing
we have done is taken our entire our library of finished shots and takes for
every single feature and short - everything we’ve ever done, before even
1995’s Toy Story - and put it all online and available and all
sitting on the VAST cluster.”
He
continued, “As you can imagine all that data in the future could be used as a
training data. We’re talking not just final images but all the setup files used
to generate those images as well. The library is valuable as training data but
the actual applications themselves don’t exist at the moment.”
The
data-intensive animation technology used to make Elemental would
not have been possible without deploying a data/storage platform from VAST
Data.
AI-Powered
Volumetric Animation
“Traditional
animation uses geometry and texture maps with the geometry deformed by a
skeletal system,” Eric Bermender, Head of Data Center and IT Infrastructure at
Pixar Animation Studios explained to IBC365. “For example, if you saw a shot of
Buzz Lightyear walking, the geometry and texture maps will be the same from
frame to frame albeit deformed in some particular way.
“Those
assets might be large but they don’t change by frame so we can cache them.
However, volumetric characters don’t have that. Every single frame is a new
simulation. We lost the ability to cache because everything is unique per frame
and the IOPS (Input/output operations per second) went up significantly.”
In Elemental directed
by Peter Sohn, characters representing the four elements (air, fire, water and
earth) live in proximity (though, of course, elements can’t mix…) in and around
a society known as Element City. These characters don’t have traditional
geometry and texture maps but are volumetric or simulated.
“This means
that every time the animation team iterates the frame it creates a new
simulation and that meant that our compute and store capacity needed started to
accelerate quickly. Instead of one geometry file and one set of character maps,
now every single frame is a unique simulation of that character.”
Pixar’s
first experiment with volumetric animation was in creating the ethereal ghost
like characters in the ‘great before’ of Soul (2020). This was
also the first project on which Pixar worked with VAST.
“With Elemental the
characters are much more animated [than in Soul] and every single
character is a volumetric character. Even some of the background set pieces and
buildings are volumetric animations. Soul was our practice
run; Elemental is the full deal.”
Faster
Storage for an AI Future
VAST uses
all-flash storage as a replacement for the 20-to-30-year-old storage paradigm
based on hard disk drives (HDD) and tape and data tiering. Its architecture
allows Pixar to have information stored in computer memory and available for
rapid access.
For
context Toy Story in 1995 utilised just under 300 computers
and Monsters, Inc (2001) took nearly 700 computers. In
2003 Finding Nemo used about 1,000.
With Elemental, the
core render farm on Pixar’s California campus boasts more than 150,000
computers to render nearly 150,000 volumetric frames and 10,000 points of
articulation for each of the main characters, Wade and Ember. By contrast,
typical Pixar character models only have about 4,000 points.
Elemental created six times the data
footprint and computational demands for data than that of Soul. By
moving 7.3 petabytes of data to a single datastore cluster VAST provides
real-time access to keep Pixar’s renderfarm constantly busy.
“In the
past, we would have to segment separate [Pixar film projects] onto separate
network interfaces,” explained Bermender. “We did that because a show that’s in
active production has historically generated the most IOPS and capacity growth
as we render out.”
However,
the new IT system now allows for shows that are in development to be able to
trial new methods of animation with an efficiency not previously possible.
“Maybe we
are working on a new environment or new character that’s never done before and
we hit go for render and it overwhelms the cluster with IOPs. Now, with VAST,
we can segment different projects with different paths to the storage and data
resource and it doesn’t slow the whole pipeline down.”
He reveals that
during production somebody had accidently made a mistake and set the whole
system to regenerate every single character and shot overnight.
“We didn’t
notice until the next morning that the system had written out as much data as
all of Toy Story 3 in a 12-hour period. The system itself was
performant and able to do it. It was pretty amazing to me that we literally
rendered out the entire footprint of a movie we only made in 2010 in just a few
hours.”
Given this
boost in rendering speed you would think that the typically lengthy multi-year
process of creating an animated feature could be reduced.
Bermender
disagreed, saying that even as compute and storage tech advances, the animators
will take advantage of that capacity to create more complex images.
“As we
create the ability to iterate faster it frees the creative process for artists
to create more complex scenes resulting in the same amount of time needed to
render an image. Animators will work on a scene during the day and send a job
to render overnight. That job has to be done by the next morning so by the time
the animators come in they can begin work on dailies.”
He added,
“Artificial Intelligence has the potential of enabling more creative and complex
images than perhaps we see now but I don’t think it will actually reduce the
time taken to render them.”
Greater
Processing Capacity adds Flexibility
The ability
to deliver large volumes of data at render time will help Pixar as it prepares
to leverage AI for future films.
For
instance, RenderMan, the Pixar-owned company that created the software that
paints the final images, recently released ML algorithm ‘Denoiser’ to the
market.
“We’ve been
using Denoiser for a long time. We take old shots and curate them and RenderMan
uses these curated copies of the images as training data so it knows how to
smooth out during path tracing. To do that successfully the denoiser has to be
‘aware’ of what is the scene.”
He says the
type of AI image manipulation that solves a practical problem is more useful
than the more generic type of image generator.
“It’s one
thing to generate an image using something like Midjourney, quite another to do
it for animation storytelling where you need to have control.”
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