NVIDIA Kaolin Library Integrates Advanced Elastic Simulation Techniques

Recent
advancements
in
generative
AI
and
multi-view
reconstruction
have
paved
the
way
for
rapid
3D
content
generation.
However,
practical
applications
in
fields
such
as
robotics,
design,
augmented
reality
(AR),
virtual
reality
(VR),
and
gaming
require
these
3D
models
to
be
manipulated
in
a
physically
plausible
manner,
according
to
NVIDIA
Technical
Blog.

Challenges
in
Traditional
Physics
Simulation

Traditional
physics
simulation
algorithms,
designed
for
well-conditioned,
manually
curated
geometries
like
tetrahedralized
volumetric
meshes,
face
significant
challenges
in
handling
the
diverse
and
often
messy
input
geometries
produced
by
modern
3D
content
generation
techniques.

Introduction
of
Simplicits
in
Kaolin
Library

To
address
these
challenges,
the
latest
release
of
the

NVIDIA
Kaolin
Library
incorporates
a
state-of-the-art
technique
called

Simplicits.
This
unified
representation
allows
for
physics
simulations
on
a
wide
range
of
geometries,
including
messy
meshes,
point
clouds,
and
learned
representations
such
as
Gaussian
Splats
and
Neural
Radiance
Fields
(NeRFs).

API
for
Different
User
Levels

The
Kaolin
physics
API
offers
two
levels
of
abstraction
for
utilizing
Simplicits.
Physics
experts
can
leverage
the

low-level
functionality,
while
generative
AI
developers
can
utilize
a
more
accessible

high-level
API.

Interactive
Simulations
and
Applications

A
demonstration
video
showcases
elastic
simulations
on
various
meshes,
including
an
interactive
simulation
of
a
chair
model
in
real-time
within
a
Jupyter
notebook.
This
capability
facilitates
rapid
prototyping
of
new
interactive
applications
for
the
diverse
geometric
representations
emerging
from
AI
research.
Additionally,
users
can
pass
splat
segments
into
Simplicits
to
simulate
complex
objects,
as
illustrated
by
the
simulation
of
a
Gaussian
splat
of
an
apple.

Advanced
Muscle
Simulation

The
video
also
highlights
muscle
simulation
using
Simplicits,
demonstrating
the
volumetric
motion
of
bones
and
anisotropic
muscle
fibers.
Simplicits
can
effectively
manage
material
heterogeneity
between
bones
and
muscles,
providing
a
robust
solution
for
simulating
complex
biological
structures.
For
further
details,
users
can
refer
to
the

muscle
simulation
tutorial.

Conclusion

Representation-agnostic
physics
simulation
is
now
available
in
the

NVIDIA
Kaolin
Library.
Interested
users
can
join
the
SIGGRAPH
2024
conference
to
learn
more
about
Simplicits
and
the
latest
3D
deep
learning
technologies
added
to
the
Kaolin
Library.

For
more
information,
users
can
engage
with
the
3D
deep
learning
community
on
the

NVIDIA
Kaolin
forum.

Image
source:
Shutterstock