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HAL
open science

(keynote) Graphics vs Physics : the light and dark sides
of the Force

Fabrice Neyret, Emmanuel Delangre

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To cite this version:

Fabrice Neyret, Emmanuel Delangre. (keynote) Graphics vs Physics: the light and dark sides of the Force. GraPhyz 2019 - Workshop physique/mécanique/informatique graphique, Oct 2019, Grenoble, France. hal-02333788

HAL Id: hal-02333788

https://inria.hal.science/hal-02333788v1

Submitted on 4 Jun 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Graphics vs Physics :
the graphics side of the force
What is the Computer Graphics domain ?

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# What is the Computer Graphics domain?

## Facets :

* Shapes (humans & animals & hairs, forests & landscape, clouds...)

* Movement (walk & crowds, skin & muscles, clothes, water & smoke, objects interactions...)

* Visuals / “rendering” (light transport simu + materials appearance)
  + sides: 3D manufacturing (3D printing), image analysis (deep learning), interface design (UI)...
# What is the Computer Graphics domain?

## Facets :

* Shapes (humans & animals & hairs, forests & landscape, clouds...)

* Movement (walk & crowds, skin & muscles, clothes, water & smoke, objects interactions...)

* Visuals / “rendering” (light transport simu + materials appearance) + sides: 3D manufacturing (3D printing), image analysis (deep learning), interface design (UI)...

## Users :

* Industries: movies (realistic or not), games, simulators (driving, medic...), impact studies (& architecture, museum), design (archi, cars...)

* Users job: interactive artist & engineer, → control, usable boss & customer → purpose. often vague spec & valid

* + end user: public
# List of requirements - mission statement

Amounts:

* 4 k² pixels * 500 k frames (+ stereo),

* ultra-complex scenes (quantitative, span, phenomena)

* time budget: 1-100 h/frame (movies)
  1/60" real-time (games) + stereo [ratio: 10⁷ !]

* memory budget: GPU, RAM, disk/cloud (+ transfer rate)
# List of requirements - mission statement

## Amounts:

* 4 k² pixels * 500 k frames (+ stereo),

* ultra-complex scenes (quantitative, span, phenomena)

* time budget: 1-100 h/frame (movies)
  1/60" real-time (games) + stereo [ratio: 10⁷ !]

* memory budget: GPU, RAM, disk/cloud (+ transfer rate)

## Constraints:

* Design user :

* almost any mixed input data (+large)

* controlable (params (+intuitive), initial configuration, reliable preview, ... result)

* action (show something, make something happen)

* End user :

* space/time continuity (no artifacts - Human visual system)

* games: reactive to unknown action

* long time span

* A good one: world seen from camera, for humans
# Diversity of approaches: eg, walking character

since diversity of purpose & target: cartoon / realistic / not-existing

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* Disney-style motion design: puppet / creative / artist intuition / choreography

* Performance capture: (+ correct / retarget)

* Math/Phys: kinematics, inverse kinematics, solve balance, motion planning, inverse dynamics, [ inverse problems ]

* Bio/Physics: biomeca ( muscles, skeleton :simu ), anticipation, nerve activation pattern.

* Simu/Phys: liquids, smoke, flesh, cloth, hairs...
# Diversity of approaches: eg, walking character

since diversity of purpose & target: cartoon / realistic / not-existing

<figure><img src="image_10.png" /></figure>

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* Disney-style motion design: puppet / creative / artist intuition / choreography

* Performance capture: (+ correct / retarget)

* Math/Phys: kinematics, inverse kinematics, solve balance, motion planning, inverse dynamics, [ inverse problems ]

* Bio/Physics: biomeca ( muscles,skeleton :simu ), anticipation, nerve activation pattern.

* Simu/Phys: liquids, smoke, flesh, cloth, hairs...

→ toolbox: multitude of scienceS, physicS, mathS models.

E.g., just for ocean:

Fourier, waves, N.S., Bernoulli, Eulerian / Lagrangian (SPH)...

( + capillary waves, spray, foam, lighting... )
# Diversity of approaches: eg, walking character

→ multitude of scienceS, physicS, mathS models.

## Reminder:

- complex, controllable, param not always available, or not the right ones

- target = motion + shape + material & light simulation

→ CG domain =

- integrative of many Sciences ( and more: arts, techs ),

- for “integrative experiments” ( e.g. sceneries )

- on which little control ( customer. less and less negotiation ).

→ required: usability, interoperability, compatibility, scalability ( computable, storable ).
# Diversity of researchers

Many domains/motivations:

( CG is more an encounter of domains than a domain )

* People/goals from industry(s), math(s), CS(s), phys, arts

- Some: care about integrativity, usability

- Some: generic toy models / “aquarium” situations

- ( me:

* “big old questions” relat. to all Humans can see for ages :-)

* Understanding / testing sciences knowledge about natural phenomena. ( transversal domain )

* “If I can reproduce a convincing cloud, means we understand it enough”

* 1st cursus in AppMaths ( scientific computing )

* From industry : CG = natural phenomena for humans

( transversal domain )

( artist / boss-customer / viewer )
# Science content is about...

equations & algorithms & representations

Science-to-engineering workflow:

- General physics → models & representations

→ Given setup & question → select subset

→ Mathematization → continuous eqn

→ prep. computing: Discretization ( → scale closure ? )

→ AppMaths: solvers

→ implem: Computer Sciences

→ CG covers the “full stack”
## How to modelize a problem / levels of modelization

* Scales & ranges ?

* Amounts ? vs time/mem budget

repr.
* Which controls/params ? which constraints/hypothesis ?
where camera can't be ?

mod. phys.
* Is physics simulation necessary ? which level ? close or open
domain ? BC ?

eqn.
discretiz.
appMaths
* Solver: eqn type/stability ? coupling ? subscales ?

algo
CS
* Integrable ? usable ? perf, / compatible // or GPU ?
Backup solutions. Conversions ( for inputs / output data )

Practical issues: short projects / hard for students / publis vs useful
transfer knowledge to/from users
# Science content is about...

equations & algorithms & representations
...and indeed, physical modelization.

* Eqn & concepts from physics:

* Copy - paste

* Adapt - simplify - analogy

* Rework deeply - complete

* + perceptions Sc. (→ new hypothesis: simplif + constraints)
# Science content is about...

equations & algorithms & representations
...and indeed, physical modelization.

* Eqn & concepts from physics:

* Copy - paste

* Adapt - simplify - analogy

* Rework deeply - complete

* + perceptions Sc. ( → new hypothesis: simplif + constraints )

* Algo & representations:

* Reminder: arbitrarily complex
    → have to be storable / computable / stable / interp. / control.

* appMaths (solver), CS (comput. models: parallelism)

* Perf: might be ignore for early sketch,
    but soon important ( even for movies, but ≠ qual crit )
    ( and to be able to work ! )
# Science content is about...

equations & algorithms & representations
...and indeed, physical modelization.

Cousin to physics & engineering. Can be a trap!
Sometime hard to communicate with physicists since:

* Shape/details are crucial
Every pixels & colors count (Seriously. wrong pixels forbidden)
ex: Nebula simulation. “biblio” inquiry

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Asking the astrophysics expert 1:

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Asking the astrophysics expert 2:

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“reality” ( ie, extrapolating better model ) from biblio

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reality = ultra-heterogeneous
physics = nonLinear(density)
# Same for clouds...

Reality:

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+ paragliders testimonies

For physicists:

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opposed experts opinion

<figure><img src="image_23.png" /><figcaption>Depiction of CAPE on a Skew-T-logP Diagram</figcaption></figure>

Θ Potential temperature of environment

Θ Potential temperature of lifted air parcel
# Same for clouds...

Reality:

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+ paragliders testimonies

→ from physics simulation:

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Issues:

- Details-induced thermodynamics

- Sharp borders / inversion: Shannon-Nyquist

For physicists:

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opposed experts opinion

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Same for water surface...

expert 1: "capillarity is always negligible"

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<figure><img src="image_33.png" /></figure>
# Science content is about...

equations & algorithms & representations
...and indeed, physical modelization.

Cousin to physics & engineering. Can be a trap !

Sometime hard to communicate with physicists since:

* Shape/details are crucial
Every pixels & colors count (Seriously. wrong pixels forbidden)

* Time & space continuity (Human visual system)

* Long time & space spans

* Integrated scope. scalability / calculability
# Science content is about...

equations & algorithms & representations
...and indeed, physical modelization.

Cousin to physics & engineering. Can be a trap !

Sometime hard to communicate with physicists since:

* Shape/details are crucial
Every pixels & colors count (Seriously. wrong pixels forbidden)

* Time & space continuity (Human visual system)

* Long time & space spans

* Integrated scope. scalability / calculability

+ human issues:

* Language exactness

* Not exact same sub-sub-field / configuration / focus

* Wrong projection about who we are / different constraints

* Projects scale / Publi: achievement required level
# Claim: C.G. IS a physic science ! ( or can be )

## Reminder: What is science

* scientificity (Popper) : Predict relation between observables for given question + setup (range, scales)

* Just: CG vs engineering vs physicists often rely to different question / use-case

## High transversal culture in math/phys/CS (individually or collectively)
[ + some naïvetés ]

* → Might help mediating between physicists or industry
* Or even, help validating Sc models ?
# Claim: C.G. IS a physic science ! ( or can be )

## Reminder: What is science

scientificity (Popper) : Predict relation between observables
for given question + setup (range, scales)

Just: CG vs engineering vs physicists often rely to different question / use-case

## High transversal culture in math/phys/CS (individually or collectively)
[ + some naïvetés ]

→ Might help mediating between physicists or industry
Or even, help validating Sc models ?

## Issue: How validating models ?

* Ourself and coworker :-p / reviewer

* Boss / customer / expert

* Pragmatic: no crash, no, artifact perf ok → what else ? :-) Well...

* Raw difference to ref. case

* "Atomic test case" / global behavior

* User studies
# Some provocative questions :-D

## Frustrations:

* Why are physicists pointless ? :-p

* what about real complexity / scenes / materials - explain every pixels

* general cases ( 3D, nearfield-connection, long time span )

* knowledge where money is ? microelec / nano / fluids vs clouds / ocean / astro

* Why can't they understand what we want ? :-D

* Why do they have blind spots about perf / mem ?
  availability of data / BC ?

* Why don't they know there own colleagues topic ? over-assume ?
  Use different units/hypothesis ? and don't even tell explicitly ?
# Some provocative questions :-D

## Frustrations:

* Why are physicists pointless ? :-p

* what about real complexity / scenes / materials - explain every pixels

* general cases ( 3D, nearfield-connection, long time span )

* knowledge where money is ? microelec / nano / fluids vs clouds / ocean / astro

* Why can't they understand what we want ? :-D

* Why do they have blind spots about perf / mem ?
  availability of data / BC ?

* Why don't they know there own colleagues topic ? over-assume ?
  use different units/hypothesis ? and don't even tell explicitly ?

## Jealousy:

* Where is the secret book of “physical principles” magic shortcuts ?

* Why Nature doesn't punish them for their invalid maths ? :-)
# Some provocative questions :-D

## Frustrations:

* Why are physicists pointless ? :-p

* what about real complexity / scenes / materials - explain every pixels

* general cases ( 3D, nearfield-connection, long time span )

* knowledge where money is ? microelec / nano / fluids vs clouds / ocean / astro

* Why can't they understand what we want ? :-D

* Why do they have blind spots about perf / mem ?
  availability of data / BC ?

* Why don't they know there own colleagues topic ? over-assume ?
  use different units/hypothesis ? and don't even tell explicitly ?

## Jealousy:

* Where is the secret book of "physical principles" magic shortcuts ?

* Why Nature doesn't punish them for their invalid maths ? :-)

## Togetherness:

* Is simulation hopeless for real problems ? ( real river boundary cond... )

* How to do real science (refut.→ Lakatos) on simulation (cosmology, climatology...)
  Should we let physicists play with it (alone) ?

* Would s.o. be interested in us (CG) *mediating* integrated physics ?

* When will computers be 1000 times more powerful ? (comput / mem)