Computer Graphics Psychophysics

1. Computer Graphics Psychophysics • Heinrich H. Bülthoff • Max-Planck-Institute forBiological Cybernetics • Tübingen, Germany

2. What is psychophysics? A research strategy to understand perception and performance by testing the relationship between the psychic (what an observer sees and reports) and the physical (patterns of light entering the eye).

3. Method Show and tell : • static images • video animations • vision + haptics • virtual environments

4. Main questions Basic Question: What image information does the brain use and what does it ignore ? Applied Question: How can we render images that convey only the information that the brain uses?

5. Examples of vision problems • Recognition • Depth perception • Navigation • (Haptics)

6. Computer Graphics Psychophysics at the MPI Tuebingen • Recognition • Shape Perception • Haptics • Driving • Navigation

7. Object recognition – how does it work? Naïve theory : First, the brain computes an object’s 3-D shape. Then the brain matches the 3-D shape to previously seen shapes that are stored in memory. image z-buffer 3-D shape memory

8. Image-based Recognition Alternative Theory: The visual system ignores depth perception when recognizing an object. Rather, the visual system recognizes an object directly from its image. z-buffer (depth perception) image image memory (recognition)

9. Recognition of Biological Motion • Recognition of point-like walker

10. Biological Motion PerceptionJohansson (1911-1998) Walter: insert orig. johannson movie here 30 sec max

11. y x Z z Depth perception from stereo is ignored Bülthoff, Bülthoff and Sinha (Nature Neuroscience 1998) • Recognition is unaffected byscrambling the depth structure • 2D motion pattern and not 3D structure is used for recognition

12. Image-based RecognitionEvidence from: • Psychophysics (Buelthoff, MPI Tuebingen) • limited generalization (30°) despite full 3D information • Physiology (Logothetis, MPI Tuebingen) • image-specific neurons in trained monkeys • Theory (Poggio, MIT) • image-interpolation networks • Rendering Application (Blanz & Vetter, MPI Tuebingen) • image-based face synthesis

13. One Object - Two InterpretationsMarkus Raetz

14. Man or Hare ? Markus Raetz

15. Why does the brain make assumptions? Images are ambiguous. Any image can be explained by several combinations of 3-D shape, material and lighting.

16. e.g. The Necker Cube

17. e.g. The Necker Cube view from above view from below

18. e.g. Depth-reversal ambiguity in shading A valley illuminated from the right looks the same as a hill illuminated from the left. valley hill

19. Prior assumption about light source

20. Mould of a footprint ?

21. Assumption that light source is stationary

23. Prior assumptions about shape • The Hollow Mask Illusion

24. 3 prior assumptions 1. light from above 2. viewpoint from above 3. shape is convex

25. Example: failure of all 3 prior assumptions shape is concave viewpoint from below light from below

26. “ Measuring Visual Shape using Computer Graphics Psychophysics” (see Workshop proceedings) convex concave (face) (mask)

27. Procedure

29. Task: hill or valley ?

30. Assumption 1 : light direction light from above light from below

31. Assumption 2 : viewpoint direction(case 1 - convex shape) view from above view from below

32. Assumption 2 : viewpoint direction(case 2 – concave shape) view from below view from above

33. Data : per cent correct scores(see workshop proceedings for more details) 87 (best) 15 (worst) 64 39

34. Lessons for rendering Illumination and viewpoint should be from above, especially if the surface is concave. Otherwise : • Shading will look weird. • Perceived shape will be incorrect.

35. Weird shading Frankenstein monster illuminated from below.

36. Face Recognition • Cyberware Scanner Texture Map Shape Map

37. MPI 3-D Face Database

38. * * * * + + + W +….. W W W 1 2 3 4 * * * * + + + W +….. W W W 1 2 3 4 View-synthesis from a single imageVolker Blanz and Thomas Vetter (SIGGRAPH ‘99) = Input Output =

39. All views from a single image

40. Synthetic ActorsVolker Blanz und Thomas Vetter (SIGGRAPH 1999)

41. Audrey Hepburn (1929-1993) New Hat + New Illumination New View Original

42. Do you recognize her ? NewView Mona Lisa

43. Virtual Tuebingen

44. Summary (Visual Recogntion) • Visual recognition is based on images, not on 3-D geometry. • More 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.

45. 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.

46. Summary (3-D Shape Perception) • Shape from shading is strongly affected by prior assumptions hard wired into the brain. • Photorealistic renderings will look weird if the scene model or viewing parameters are inconsistent with these assumptions.

47. Data (see workshop proceedings) • 100 • light-from-above • light-from-below • Percent • correct 50 view-from-above • view-from-below • convex • concave 0