Enhancing Perception in Physically Simulated Human Animation

1. AnimationCS 551 / 651 Perception of Physically Simulated Humans Hodgins et al., 1998

2. Assignment 1 Suggestions • Emphasize development of physics code • Even without graphics (simulate a 2D disk on a plane) • Apply force (hard coded) to COM to show how forceaccvelchange in position • Apply force offset from COM to show ang. effects • Just use printf to verify success • 2D dynamics (linear and angular) + integrator • Start with Euler

3. Assignment 1 Suggestions • Test collision code in simplest scenario • 2D Disk hitting a wall • Just use distance from COM to plane to measure collision

4. Assignment 1 Suggestions • Scale to 3D • Extension to 3D should go smoothly • You could stay in an environment without graphics • Don’t need file loader to initialize models • Just define one in code

5. Assignment 1 Suggestions • Adding on the rest • File loader (use simple models) • Runge-Kutta • Interface • Start simple (without any toolkit, just control the vector using GLUT mouse tracking – aimed straight into monitor)

6. Paper Categories • Perception • Creating motion automatically using optimization • Simulation • Multilink systems • Simplification of simulation • Motion capture • Blending and reusing segments • Kinematics • Low-level motion like walking/running/jumping • Gait generation, abstraction, reuse, biomechanics • High-level motion like generating paths • Generating group behaviors

7. Perception • How should we render our objects? • To what end? • Verisimilitude • Mechanical accuracy • Impressionism • What about motion?

8. Is simplicity better? • Advantages • Abstraction is easier • Obfuscations are removed • Disadvantages • Complementary features are removed • Edges, critical features • May look “wrong”

9. Is complexity better? • Advantages • Details provide perceptual cues • This is the way we perceive things in real world • Disadvantages • Difficult to get the details right • May distract from basic motion

10. We have no idea… • We turn to different experts • Psychologists • Automated computer vision

11. Psychologists • Kubovy and Proffitt @ UVa • Perception of patterned dot animations • Models of perception • Perception as it relates to action • We perceive because it helps us to act • Attacks the perception question within well-defined psychological models

12. Computer Vision • Martin and Acton @UVa • Low-level vision • How do we detect edges, shadows, primitives • High-level vision • How do we compose “things” from primitives

13. Still no solid answers • Vision and psychology provide models of perception that influence graphics • Graphics permits isolated experimentation with perception models • The three fields move forward together

14. What’s amazing about us? • Perceiving friends • Just two moving lights on ankles is enough • Just two seconds is required • Perceiving pendula • Humans thought moving dots were connected via flexible bar, not rigid pendulum

15. Hodgins’ comparison • Is there a difference?

16. Observational tests • Torso rotation • Keep head looking forward, but rotate torso and arms • Arm Motion • Make arm swing more forward / backward • Adjust dynamics accordingly • How much? • Noise • Randomly perturb joint angles (waist, shoulders, neck) • No dynamics • How much?

17. How the simulation works

20. Experimental protocol • Watch animations in pairs • 4 seconds of one then 4 seconds of a mate • Indicate similarity or difference within pair • forced choice • Could you forget what first looked like? • Approx. 25 people per condition • Varied the order • Avoids ordering effects (learning during experiment)

21. Experimental protocol • Animations rendered in same way • Could this have made a difference? • Is there a rendering that is conducive to stick figures? • What tricks would people use to identify motions? • Played from VHS at 30 fps • Can’t have any effects from rendering blips

22. Results • On average, people were better with manH

23. Results • But how did rendering affect each person’s ability? There’s a trick!

24. Take-away messages • Don’t read too much into these results • Each experiment may be different • More detailed model was also more human-like • Standardization of animation environments might be good for comparison • Difficult to compare improvements from year to year

25. What else matters? • Camera movement • Ground plane • Motion blur • Secondary motion (clothing / hair) • Shadows

26. Additional commentary • Experiments are essential for graphics • Yet rarely conducted • How is graphics evaluated? • The SIGGRAPH “aaahhhh” factor

27. Additional commentary • Creating experiments is dicey business • Have to include psychologists who are experts of experiment design • Make sure enough subjects are included • You need to understand the domain so well that you know the answer before the experiments are complete • Many pretrials were conducted to refine amounts of noise to add (to avoid making it too easy or hard)

28. Follow-up paper • Bodenheimer et al., 1999 Eurographics Animation Workshop • How does noise influence perception?

29. How to add noise to simulation? • Sensors • When the arm reaches angle q, trigger reaction • Control gains • How stiff/strong are the muscles • Output torques • How regular and well-behaved are the muscles • Control parameters • When does the arm swing backwards

30. Output torques • Noise inserted here didn’t work well • Instantaneous noise was quickly corrected with subsequent countertorques

31. What kind of noise? • Variability of human motion is tied to large movements of the body • Not a random sinusoidal noise function • Not a white noise

32. Experimental scenario • Watch 10 movies of varying noise and select the one that looks most “natural”