Computer Graphics Psychophysics

Heinrich H. Bülthoff

Max-Planck-Institute forBiological Cybernetics

Tübingen, Germany

Heinrich H. Bülthoff

Max-Planck-Institute forBiological Cybernetics

Tübingen, Germany

What is psychophysics?

A research strategy to understand perception and A research strategy to understand perception and performance by testing the relationship between the performance by testing the relationship between the psychicpsychic (what an observer sees and reports) and the (what an observer sees and reports) and the physicalphysical (patterns of light entering the eye). (patterns of light entering the eye).

A research strategy to understand perception and A research strategy to understand perception and performance by testing the relationship between the performance by testing the relationship between the psychicpsychic (what an observer sees and reports) and the (what an observer sees and reports) and the physicalphysical (patterns of light entering the eye). (patterns of light entering the eye).

Method

Show and tell :Show and tell :– static imagesstatic images

– video animationsvideo animations

– vision + haptics vision + haptics

– virtual environmentsvirtual environments

Show and tell :Show and tell :– static imagesstatic images

– video animationsvideo animations

– vision + haptics vision + haptics

– virtual environmentsvirtual environments

Basic Question:Basic Question:

What image information does the brain use and What image information does the brain use and what does it ignore ? what does it ignore ?

Applied Question:Applied Question:

How can we render images that convey only the How can we render images that convey only the information that the brain uses? information that the brain uses?

Basic Question:Basic Question:

What image information does the brain use and What image information does the brain use and what does it ignore ? what does it ignore ?

Applied Question:Applied Question:

How can we render images that convey only the How can we render images that convey only the information that the brain uses? information that the brain uses?

Main questions

Examples of vision problems

– RecognitionRecognition

– Depth perceptionDepth perception

– NavigationNavigation

– (Haptics)(Haptics)

– RecognitionRecognition

– Depth perceptionDepth perception

– NavigationNavigation

– (Haptics)(Haptics)

Computer Graphics Psychophysics at the MPI Tuebingen

• Recognition Recognition

• Shape Shape Perception Perception

• HapticsHaptics

• DrivingDriving

• NavigationNavigation

• Recognition Recognition

• Shape Shape Perception Perception

• HapticsHaptics

• DrivingDriving

• NavigationNavigation

Object recognition – how does it work?

Naïve theory :Naïve theory :

First, the brain computes an object’s 3-D shape.First, the brain computes an object’s 3-D shape.

Then the brain matches the 3-D shape to previously Then the brain matches the 3-D shape to previously seen shapes that are stored in memory. seen shapes that are stored in memory.

image z-buffer 3-D shape memoryimage z-buffer 3-D shape memory

Naïve theory :Naïve theory :

First, the brain computes an object’s 3-D shape.First, the brain computes an object’s 3-D shape.

Then the brain matches the 3-D shape to previously Then the brain matches the 3-D shape to previously seen shapes that are stored in memory. seen shapes that are stored in memory.

image z-buffer 3-D shape memoryimage z-buffer 3-D shape memory

Image-based RecognitionAlternative Theory:Alternative Theory:

The visual system ignores depth perception when recognizing an The visual system ignores depth perception when recognizing an object. Rather, the visual system recognizes an object directly object. Rather, the visual system recognizes an object directly from its image. from its image.

z-buffer (depth perception)z-buffer (depth perception)

imageimage

image memory (recognition)image memory (recognition)

Alternative Theory:Alternative Theory:

The visual system ignores depth perception when recognizing an The visual system ignores depth perception when recognizing an object. Rather, the visual system recognizes an object directly object. Rather, the visual system recognizes an object directly from its image. from its image.

z-buffer (depth perception)z-buffer (depth perception)

imageimage

image memory (recognition)image memory (recognition)

Recognition of Biological Motion

Recognition of point-like walkerRecognition of point-like walker

Biological Motion PerceptionJohansson (1911-1998)

Walter:

insert orig. johannson movie here

30 sec max

Walter:

insert orig. johannson movie here

30 sec max

Depth perception from stereo is ignored Bülthoff, Bülthoff and Sinha (Nature Neuroscience 1998)

Recognition is Recognition is unaffected byunaffected byscrambling the depthscrambling the depth structure structure

2D motion 2D motion pattern and pattern and not 3D structure not 3D structure is used is used for recognitionfor recognition

Z

y

zx

Image-based RecognitionEvidence from:

• Psychophysics (Buelthoff, MPI Tuebingen)Psychophysics (Buelthoff, MPI Tuebingen)

– limited generalization (30°) despite full 3D informationlimited generalization (30°) despite full 3D information

• Psychophysics (Buelthoff, MPI Tuebingen)Psychophysics (Buelthoff, MPI Tuebingen)

– limited generalization (30°) despite full 3D informationlimited generalization (30°) despite full 3D information

• Physiology (Logothetis, MPI Tuebingen)Physiology (Logothetis, MPI Tuebingen)

– image-specific neurons in trained monkeys image-specific neurons in trained monkeys

• Physiology (Logothetis, MPI Tuebingen)Physiology (Logothetis, MPI Tuebingen)

– image-specific neurons in trained monkeys image-specific neurons in trained monkeys

• Theory (Poggio, MIT)Theory (Poggio, MIT)

– image-interpolation networksimage-interpolation networks

• Theory (Poggio, MIT)Theory (Poggio, MIT)

– image-interpolation networksimage-interpolation networks

• Rendering Application (Blanz & Vetter, MPI Tuebingen)Rendering Application (Blanz & Vetter, MPI Tuebingen)

– image-based face synthesisimage-based face synthesis

• Rendering Application (Blanz & Vetter, MPI Tuebingen)Rendering Application (Blanz & Vetter, MPI Tuebingen)

– image-based face synthesisimage-based face synthesis

One Object - Two InterpretationsMarkus Raetz

Man or Hare ? Markus Raetz

Why does the brain make assumptions?

Images are ambiguous. Any image Images are ambiguous. Any image can be explained by several can be explained by several combinations of 3-D shape, material combinations of 3-D shape, material and lighting.and lighting.

Images are ambiguous. Any image Images are ambiguous. Any image can be explained by several can be explained by several combinations of 3-D shape, material combinations of 3-D shape, material and lighting.and lighting.

e.g. The Necker Cube

e.g. The Necker Cube

view from above view from below

e.g. Depth-reversal ambiguity in shading

A valley illuminated from the right looks the same as a A valley illuminated from the right looks the same as a hill illuminated from the left.hill illuminated from the left.

A valley illuminated from the right looks the same as a A valley illuminated from the right looks the same as a hill illuminated from the left.hill illuminated from the left.

hillvalley

Prior assumption about light source

Mould of a footprint ?

Assumption that light source is stationary

Prior assumptions about shape

The Hollow Mask IllusionThe Hollow Mask IllusionThe Hollow Mask IllusionThe Hollow Mask Illusion

3 prior assumptions

11. light from above 2. viewpoint from above . light from above 2. viewpoint from above

3. shape is convex3. shape is convex

11. light from above 2. viewpoint from above . light from above 2. viewpoint from above

3. shape is convex3. shape is convex

Example: failure of all 3 prior assumptions

shape is concave viewpoint from below shape is concave viewpoint from below

light from below light from below

shape is concave viewpoint from below shape is concave viewpoint from below

light from below light from below

“ Measuring Visual Shape using Computer Graphics Psychophysics” (see Workshop proceedings)

convex concaveconvex concave

(face) (mask)(face) (mask)

convex concaveconvex concave

(face) (mask)(face) (mask)

Procedure

Task: hill or valley ?

Assumption 1 : light direction

lightlight

fromfrom

aboveabove

lightlight

fromfrom

belowbelow

lightlight

fromfrom

aboveabove

lightlight

fromfrom

belowbelow

Assumption 2 : viewpoint direction(case 1 - convex shape)

view from aboveview from above

view from belowview from below

view from aboveview from above

view from belowview from below

Assumption 2 : viewpoint direction(case 2 – concave shape)

view from belowview from below

view view from abovefrom above

view from belowview from below

view view from abovefrom above

Data : per cent correct scores (see workshop proceedings for more details)

87(best)

15(worst)

64 39

Lessons for rendering

Illumination and viewpoint should be from above, Illumination and viewpoint should be from above, especiallyespecially if the surface is concave. if the surface is concave.

Otherwise :Otherwise :

- Shading will look weird. Shading will look weird.

- Perceived shape will be incorrect.Perceived shape will be incorrect.

Illumination and viewpoint should be from above, Illumination and viewpoint should be from above, especiallyespecially if the surface is concave. if the surface is concave.

Otherwise :Otherwise :

- Shading will look weird. Shading will look weird.

- Perceived shape will be incorrect.Perceived shape will be incorrect.

Weird shading

Frankenstein monster illuminated from below.Frankenstein monster illuminated from below.Frankenstein monster illuminated from below.Frankenstein monster illuminated from below.

Face Recognition

Cyberware Scanner Texture Map Shape MapCyberware Scanner Texture Map Shape Map Cyberware Scanner Texture Map Shape MapCyberware Scanner Texture Map Shape Map

MPI 3-D Face Database

View-synthesis from a single imageVolker Blanz and Thomas Vetter (SIGGRAPH ‘99)

Input

Output

W2*+W1* W3*+ W4*+ +…..=

=W2*+W1* W3*+ W4*+ +…..

All views from a single image

Synthetic ActorsVolker Blanz und Thomas Vetter (SIGGRAPH 1999)

Audrey Hepburn (1929-1993)

New Hat + New Illumination

Original New View

Do you recognize her ?

Mona Lisa New View

Virtual Tuebingen

Summary (Visual Recogntion)

• Visual recognition is based on images, not on 3-D geometry.Visual recognition is based on images, not on 3-D geometry.

– More texture memory not polygonsMore texture memory not polygons

Better to apply a texture map from a previously seen image than to re-render an object or scene from a new viewpoint or under a new lighting condition.

• Visual recognition is based on images, not on 3-D geometry.Visual recognition is based on images, not on 3-D geometry.

– More texture memory not polygonsMore texture memory not polygons

Better to apply a texture map from a previously seen image than to re-render an object or scene from a new viewpoint or under a new lighting condition.

Summary

• Does the brain use a depth buffer?

– Yes: for shape perception (shape from shading)

– No: for recognition (biological motion)

• Stereo goggles not necessary for recognition.

• Does the brain use a depth buffer?

– Yes: for shape perception (shape from shading)

– No: for recognition (biological motion)

• Stereo goggles not necessary for recognition.

Summary (3-D Shape Perception)

• Shape from shading is strongly affected by prior assumptions Shape from shading is strongly affected by prior assumptions hard wired into the brain.hard wired into the brain.

• Photorealistic renderings will look weird if the scene Photorealistic renderings will look weird if the scene model or viewing parameters are inconsistent with these model or viewing parameters are inconsistent with these assumptions. assumptions.

• Shape from shading is strongly affected by prior assumptions Shape from shading is strongly affected by prior assumptions hard wired into the brain.hard wired into the brain.

• Photorealistic renderings will look weird if the scene Photorealistic renderings will look weird if the scene model or viewing parameters are inconsistent with these model or viewing parameters are inconsistent with these assumptions. assumptions.

100100

light-from-abovelight-from-above

light-light-from-belowfrom-below

PercentPercent

correct 50correct 50 view-from-above view-from-above

view-view-from-belowfrom-below

convexconvex

concaveconcave

Data (see workshop proceedings)

0