Hello, and welcome to the Physics World weekly
podcast,
the last episode of 2025.
And it features Pat Hanrahan,
who studied
nuclear engineering and biophysics
before becoming a founding employee
of Pixar
Animation Studios
in the nineteen eighties.
That conversation is coming up after this message
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Earlier this year, Physics World's Margaret Harris attended
the Heidelberg
Laureate
Forum in Germany.
This international event brings together early career researchers
with some winners of the most prestigious
awards in mathematics
and computer science.
There, she spoke to a laureate who's also
won three academy awards. Here's that conversation.
So I'm talking to Pat Hanrahan, who won
the Association for Computing Machineries AM Turing Award
in 2019
for contributions to three d computer graphics or
CGI.
So you're best known for that type of
work, but you actually started out, I think,
in nuclear engineering Yes. And then you got
a PhD in biophysics. Yes. So how did
you get into those subjects? Tell me about
nuclear engineering first. Well, when I was in
school, I I really felt I wanted to
become a physicist. Anyway, I like math too,
and I like science in general, but I
think physics was the field that was most
interesting to me. And so, yeah, when I
went to the University of Wisconsin actually, it
was it was it was doubly nice because
they had this really nice, what they call,
honors program,
where you have a lot of freedom in
what classes you took and all that. So,
you know, not just take physics, but do
other things as well. Take chemistry, biology, other
sciences.
So yeah. So I was always interested in
physic. I love my intro physics course. I
can still
remember the lectures and, you know, going through
it all. And,
yeah. So I just loved physics, you know,
all the way through the undergraduate
physics, mechanics,
electrodynamics,
quantum mechanics. I just loved all those courses,
my favorite courses.
But why nuclear engineering specifically? Why not? Well,
I think it was mostly because of this
honors program. I was actually I actually and
I when I went there, the chairman of
the department,
offered me a job,
and he was the chair of the nuclear
regulatory commission at the time. So I ended
up, you know, in in this department. But,
you know, I was he was working for
the nuclear regulatory commission. This was, you know,
like, in the late seventies.
You know, nuclear power was having you know,
people were pretty down on that. Right? And
when I was reading about all this stuff,
I got pretty down on it too because
they weren't really addressing a lot of the
currents. I think that's maybe changing a little
bit, but I was definitely
not that interested especially at towards the end.
So I wanted to work on other areas
of physics.
I did a lot of theoretical physics. You
know? Like, when I was a graduate student,
I I had to take, like, I don't
know, five courses in quantum mechanics, you know,
quantum field theory. You know? I was really
headed towards the theoretical physics thing, but I
don't know what you I I the the
low level of physics just wasn't really exciting
me. So I I wanted to be a
physicist, but I
wanted to work on sort of higher level
problems.
So I I admired, like, Francis Crick, and
he was, like, a theoretical
biologist in some sense.
And I thought, well, maybe I could become,
like, a theoretical biologist.
And then I met this person,
and I was, he had was one of
the founders of the computational neuroscience program.
Or well, I'm sorry. Not computational neuroscience. That
didn't exist. Neuroscience program. And, he was working
on neurobiology of nematodes. He had worked for
Sydney Brenner.
And so I thought, well, I could I
could do you know, he and they weren't
being very quantitative, and I thought I could,
like, apply what I know about physics to
help them, you know, build up models of
the nervous system. And I I started doing
some simulations and things like that. Yeah. And
is that about the time that you got
first involved in Pixar, the project that became
the first Toy Story film? No. That took
a little while. I mean, you know, you
have to realize computer graphics didn't exist then.
It really didn't exist. I mean, you know,
it just did not exist. My advice so
it turned out it's sort of funny story.
Sydney Brenner, you know, Nobel laureate in biology.
There was he had two a bunch of
colleagues and students, Francis Crick. There was another
guy named John White and another guy named
John Sulston.
And John White is famous for
determining,
the structure of the nervous system of c
l. So they all worked on C. Elegans,
which is a a free living nematode.
So, John White had, like, figured out the
entire wiring grant by sectioning the words and
worm and tracing all the neurons. And,
John Solston had, worked on
developmental biology, and he recorded, you know, how
the cells
divided, you know, from an egg all the
way to a full grown one.
And, anyways, the three of them all got
really the same computers.
And,
and so, you know, my my adviser was
in that group, and he was trying to
convince me to work on computers.
And I didn't have any background in computers.
So I started simulating,
this nervous system,
and I had you, write these programs that
simulated. I was, like, solving,
like, basically cable equations for neurons because these
neurons are, like, you know, electrical cables.
But I needed to do visualization. I had
to be able to see my result. Like,
you just so my roommate introduced me to
computer graphics. He said, oh, by the way,
do you know about this graphic stuff?
You know, I just sort of got enchanted
by what I could do with it because
I could take all my theories, like, whether
it was simulation of neurons or whatever I
was interested in and see it, you know,
make it really concrete. And it just became
even more fun. You know? Like, the the
physics was suddenly
infinitely more fun because I could see it.
You know? Like, I could do animations,
not much animation, but I could at least
make pictures and so on. So I sort
of got seduced by that idea, and it
it was a wide open field. You know?
Physics and, you know, biology was, you know,
sort of slow paced. This was, like, completely
wide open. So, like like like, the first
week I worked on it, I, like, wrote
a paper. I got accepted in the major
conference. You know? Whereas in physics, you know,
I'd been a grad student for a couple
years, never written a paper. And so then
I decided I had you go learn more
about computer graphics,
but, you know, because there was nobody there.
There was only one or two places in
the world that was doing computer graphics, and
eventually, I ended up at Pixar a couple
years later. Yeah.
And a lot of your research on computer
generated imagery focuses on getting the simulated world
to display
things in the way it's grounded in physics.
Yeah. Yeah. You know, you literally wrote the
book about physically based rendering. Yeah. What are
the benefits of that approach, and what are
some of the main challenges in it? That's
a great question.
You know, I love that question. I mean,
you know, you know, we're we're creating a
virtual world, and the world, you know, has
physics in it. I mean, you know, and
just imagine. I mean, what we did, we're
just trying to create a world that, like,
if somebody looked at it had all the
phenomena that we think of in physics and
even biology as well, but all these phenomena.
So, you know, like, what are some of
the found well, a cloud might move. Right?
Wind will move the leaves of the tree.
You know, you could have an object, a
rock falling.
You know, get out something a building kind
of blowing apart. You could have, you know,
cloth, you know, conforming to somebody's skin. You
have the muscles moving the body. I mean,
there's just physics everywhere in the world. Right?
And so, you know, if we're trying to
simulate all I mean, we're trying to make
a picture of all this stuff. I mean,
how else would you do it? I mean,
you you have to in some sense, you
have to somehow capture the physics of the
everyday world. It's I I call it the
everyday world because it's at the human scale.
You know? It's what we see around us.
And I mostly worked on,
simulating the appearance of it, right, which is
like, you know, you know, why does it
look the way it does? Like, why how
do, like, materials scatter light, and how does
light get transported through the environment, and, you
know, even how cameras simulate cameras and simulate
light sources. So, yeah, so if you look
at the what we actually ended up doing,
if you look at that book, you know,
we write down
a bunch of equations that describe the scattering
of light off surfaces and off infinite media,
and then we just solve this
solve for the lightning distribution in the world.
You know? And it's a just a giant
computational science problem.
But the challenge is you have all these
real world materials,
which are both complex.
You know, they're not just like ideal mirrors
and, you know, one type of cars where
I just, like, opened up an optics book
and said, how much is in this book
that would help us do this? Almost nothing.
You know what I mean? Because, you know,
how does light scatter off skin? How does
light scatter off a rock? How does light
scatter off a cloud? Just not detailed stuff
in there. So we'd often have to come
up with, you know,
approximations
and models
for all these things. And I worked a
lot. Like, I I worked on my papers
as I'm modeling skin.
Hair. How does light scatter from hair? How
does light scatter from cloth? Stuff like that.
But, anyways, when it all is said and
done, you get, you know, like, a billion
objects
each with really complex materials,
and then you have to solve for the
lighting distribution,
which is really complicated because the objects have
complicated shape. So we use Monte Carlo techniques.
You know? In fact, just like what they
did up during the war to simulate the
neutron transport,
And that's actually was the connection back to
my work in nuclear engineering. I had learned
about transport theory and Monte Carlo techniques, you
know, in school. And so, you know, I
I sort of also just sort of gravitated
towards those problems because those are the ones
I knew to sell.
So you actually won, one of your three
Academy Awards, Oscars, in other words Yeah. Is
specifically for developing that software that simulates the
subsurface scattering of light Yes. Yeah. In in
materials that are translucent. Yeah. Yeah. Why is
that so difficult, and why is it so
important
for film and computer graphics? Yeah. Well, okay.
Why is it important? Well, I'm looking at
you, and your skin looks really healthy.
I mean, you know, I mean, you know,
in real people, skin is really important. Right?
It has to look right. And until then,
it looked like plastic.
You know, it looked like, yeah, just were
completely covered in makeup. You know? You know,
makeup and things like that, you know, almost
like pink. You know what I mean? They're
just very matte and, you know, very I
mean, you know, that's why they're matte. They're
not they did not you know, don't have
all the complexity of real skin, but real
skin has oil. It has texture to it.
It has
a bunch of properties that are very different
than these idealized plastic like, paint like material.
So so, obviously, for the movies, they want
people to look, you know, natural and appealing.
So that was one that was sort of
a motivation for it. What was interesting, the
discovery was that
most materials
are actually somewhat translucent, and subsurface scatter is
actually more common than just normal surface scatter.
So what happened in surface scatter, which you
learn about in physics, you know, light ray
hits the surface and just gets reflected off
the interface between, say, the air and the
the material itself. Yeah. Yeah. But in most
materials, they're actually dielectric. And by the way,
that's what happens in the metal. You know?
Like, light can't go into a metal because
it's conductive,
so it has to get reflected.
Okay? That's why metals are shiny. But most
materials are dielectrics. You know? They have an
index of refraction. And so most light goes
into the materials. It doesn't get reflected. You
know? You just look at it physically. And
why are the dialectics? Well, they're rocks. You
know? They're based on water. Like, we have
a lot of water in our human body.
You know? They're made of things like fibers,
anything like biological.
And most minerals are not metals. They're,
you know, they're dielectric. So almost everything around
us is a dielectric,
and what happens is the light goes into
the material and then it scatters around a
little bit, and then it comes up. It
looks like it's reflecting off it. Let's actually
go on and scatter around, come out, And
that's called subsurface reflection.
You know, it's been studied, but it's never
really been studied very much in natural no
normal everyday materials like skin or whatever. So
we we develop you know, like a physicist,
you know, just, you know, we read models
about how you might do that. I used
to read books like people like in atmospheric
sciences study that, meteorology study that, like, if
you observe a planetary atmosphere
from a distance, like, it's like you have
this layer of gas around the planet,
and so it's gonna be sort of like
that. The light goes into the atmosphere and
gets scattered. So there, you know, there's things
known about it, but they were all like
things like that, like, you know, planetary atmosphere.
So we yeah. We we just took a
lot of those ideas that they had developed
and and, you know, sort of, you know,
mod used them to model things like skin.
And in the same thing with hair, what
happens is hair is mostly,
translucent as well. It's like a little optical
fiber. And so light goes into it, bounces
around, and comes out. So these just lead
to very different appearances than
metals and things like that. So yeah. So
I I did a lot of work on
on, you know, just modeling different materials. And
the subsurface scattering works, I mean, since everything
looked like plastic, as soon as we wrote
the paper, I mean, every studio in the
world immediately,
adopted them. And we we published the source
code, and it was relatively simple model, so
they could just, like, take it and put
it into their system.
And so yeah. So they like, within a
year or two, everybody was using it sort
of very quickly. And but, you know, it's
it was I think it was the first
time an academic paper had ever gotten an
Academy Award.
What would you say is the most difficult
CGI problem either you've solved or you've seen
someone else solve by some building on your
work?
Well, I I I think the
I think the hardest thing to solve
was not just, like, how to make, you
know, skin look right. I mean, that's really
insane, hard problem.
It's how to build a system
that would include everything.
You know what I mean? Like, it's like
this it's like sort of grand unified theory.
Right? How can I build one rendering system
that would handle any shape
and any material and any light source,
you know, and any kind of,
you know, participating media and and just build
a system that could do it all? And
the problem is you if you if you
can't do a bunch of stuff, like, say
you couldn't do the atmosphere.
I mean, it's just not gonna be useful.
Right? And if you can't do people, it's
not gonna be useful. You can't do plants.
You know, like plants were a huge problem
because there's so many leaves and they have
such complicated scattering, but you have billions of
them. You know? Billions of leaves and
dandelions and stuff. So, you know, just having
a system that could handle all this stuff
and scale to the complexities,
I mean, it's it's mostly an engineering problem,
but it was it was a conceptualization
problem. Like, how do you break down,
you know, the world in some sense
into all these what are some you know,
you know, we sort of use, like, object
oriented, but what are all the objects you
need, and how are they all interrelated? How
do they all work together? And then so
that you can, like, plug in something new.
Like, when we had that skin model, like,
you just plug it in. You know, the
system had the design. So that was actually
my first academy where we had this system
we called Reyes,
R E Y E S, for the California.
There's this park called Point Reyes.
It's a very beautiful place. It was near
Lucasfilm, so everybody knew about it. But it
it was the acronym for renders everything you
ever saw. So, you know, the first thing
what I mostly worked on at Pixar was
just how to build this system. You know?
How how to build a system that renders
everything you ever saw. And, you know, it's
it was very,
very challenging and non trivial to think of
how to design this thing so it would
work. And it's still being used. I mean,
I did this many, many years ago. But,
you know, once you get the basic framework
in place,
you know, you can just keep enhancing it
because you can add stuff to it and
they it just sort of you know, you
have the right design when something new comes
along and just sort of clicks into place.
You know what I mean? So that that
I think that was the hardest part. I
mean, you know, maybe not quite as mathematical,
but just how to conceive of this thing
in a way that, you know, was both
conceptually elegant, but also worked. You know what
I mean? It could be efficient and and
and so on. So that I think that
was the hardest problem. Yeah. What would you
say then is the the most difficult unsolved
problem? What what can't you do now that
you really like to be able to do
in computer graphics?
Well, I think that I have a couple
of opinions. I mean, you know, on the
on the so we would call this photorealistic
rendering and so on. I mean, I don't
I mean, pretty much, you know, we've been
working on it so long. I mean, I
don't know if you go to movies. Some
of the people do, but, you know, the
movies are pretty convincing at this point. You
know? And even like Toy Story was the
first one that we did. It was a
cartoon movie.
Now, you know, you see movies
routinely that just
is completely indistinguishable
from,
you know, from things like Avatar and, you
know, all these movie. So it's pretty hard.
I mean, I don't know if I can
think of really,
one thing. I find natural scenes, outdoor scenes,
sort of still somewhat unsatisfying.
I mean, remember, they still fake a lot
of stuff. They don't model everything
and so on, so they can avoid, you
know, showing you things that they can't do.
But I find, you know, really if I
take a walk in the forest or something,
you know, there's something about it that I
don't I don't see in the computer, but
I can't quite pin it down. That's the
other problem. What? You're you're looking at this
stuff. Like, you look I'm looking at skin.
Like, what are we not getting about it?
You know? There's something underlying the phenomena of
the scattering. So I don't quite know exactly
for these
natural,
environments what we're getting wrong. But, anyway, I
think that's one. But I actually think, you
know,
a more interesting thing is not just photorealistic
effects, but, you know, how to make it
easy. You know, like, I gave a talk
once about starting with Leonardo, how, you know,
we sort of reproduced what Leonardo had discovered,
right, like perspective and shading and all that
stuff, and we just computerized it.
But then the event art sort of went
in lots of different directions. It wasn't just
about realism. You know? So, like, maybe how
to make other kinds of pictures,
like, either more abstract kind of pictures like
an artist might do, or more functional kind
of pictures, like, in visualizations
of things. You know, just how you know,
because there you're not just simulating the physics.
You have to ask but you still you
know, it might be some formal technique. Let's
say we had some theory of aesthetics
or something like that. Maybe we could formalize
that and have a computer do that kind
of stuff. Yeah. You see some of that
generated by AI right now. But, yeah, just,
you know, other there are different kinds of
picture.
And I think we could make a lot
of other interesting pictures besides just sort of
photographic,
You know, sort of a classic thing, like,
first we once we had a camera, like,
you know, what do we why do we
need to you know, we don't want it
to we have cameras to take realistic pictures.
We don't need, you know, artists to draw
perfectly realistic pictures. Yeah. So you're still looking
for the Pixar version of Picasso. Yeah. Yeah.
Exactly. Yeah. Yeah. Yeah. Exactly. Exactly.
So final question. How if you could just
sum it up, how do you think your
training in physics has helped you in your
career?
Well, I certainly learned a lot of phys
you know, physical phenomena, you know,
like transport,
light optics,
and mechanics, classical mechanics.
You know, lots of, particular things I learned.
But the best thing I learned was how
to solve problems.
I mean, you know, I mean, you just
you know, the problems they gave you to
solving, you just had to solve. And there
was a certain way of solving them. You
know? How do you like, you notice I
use the word modeling and simulation a lot.
You know? Like, you have physical models and,
you know, models are useful, but they're not
the whole thing. You have to, you know,
you have to pick the right model
for, you know, you know, like, start out
with really phenomenological
like models, but they can still be super
useful when you also want more fundamental models.
But there's many models, and you have to
pick the right one. And then and so
you just have to pick the right one.
And, you know, a lot of people, they
they just they don't know how to pick
the right model. You know what I mean?
And then you and then you have to
crank through whatever
it takes to sort of make it solvable.
You know what I mean? Like, you know,
it might involve spherical harmonics or it might
involve a bunch of stuff. And then some
of that stuff sort of just difficult. Right?
You just gotta have the the you have
to be, like, powerful problem solver to actually
turn that into, like, something that works. So
I think that's what I learned in physics.
And, you know, that skill,
it was I just think that's why physicists
are so successful when they go to other
fields, is they just have this magical ability
to solve problems in other fields. Yeah.
That kind of fun. Thank you very much.
Yeah. Thank you.
That was Margaret Harris in conversation with Pat
Hanrahan,
who has won three Oscars
in addition to the ACM's
AM Turing Award.
Thanks to both of them for a fascinating
conversation.
And thank you for listening to the Physics
World weekly podcast
in 2025.
I'm Hamish Johnston,
and our producer is Fred Iles.
We'll be back again
in 2026.