1. Constrained Synthesis of Textural Motion for Animation Shmuel Moradoff Dani Lischinski The Hebrew University of Jerusalem

2. How to obtain motion for character animation? Skilled animator. Skilled animator.

3. How to obtain motion for character animation? Skilled animator. Skilled animator. Motion capture system. Motion capture system.

4. How to obtain motion for character animation? Skilled animator. Skilled animator. Motion capture system. Motion capture system. Simulation. Simulation. Faloutsos et al. 02`

5. Goal A new tool for motion synthesis / re-use A new tool for motion synthesis / re-use Desirable features: Desirable features: The new motion may be as long as we want. The new motion may be as long as we want. Allowing hard constrains. Allowing hard constrains. Fast and easy to use. Fast and easy to use. Simple input. Simple input.

6. Previous Work Signal processing techniques: Signal processing techniques: Laplacian pyramid (Bruderlin & Williams 95’). Laplacian pyramid (Bruderlin & Williams 95’). Fourier transform (Unuma et al 95’). Fourier transform (Unuma et al 95’). Problems: Problems: Manually intensive Manually intensive Resulting motion isn’t always realistic Resulting motion isn’t always realistic No support for constraints No support for constraints

7. Previous Work Constraint-based motion editing. Constraint-based motion editing. Retargeting motion, motion adaptation and motion path editing (Lee & Shin 99’, Gleicher 98’ 01’). Retargeting motion, motion adaptation and motion path editing (Lee & Shin 99’, Gleicher 98’ 01’). Problems: slow, manually intensive. Problems: slow, manually intensive.

8. Previous Work Style machines – a high level tool (Brand & Hertzmann 00’). Style machines – a high level tool (Brand & Hertzmann 00’). Problems: no low-level control, need large training data. Problems: no low-level control, need large training data.

9. Previous Work Texture synthesis techniques. Texture synthesis techniques. Add details from a mo-cap data (Pullen & Bergler 00’, 02’). Add details from a mo-cap data (Pullen & Bergler 00’, 02’). A two level statistical model (Molina et-al 00’ and Li et-al 02’). A two level statistical model (Molina et-al 00’ and Li et-al 02’).

10. Previous Work Motion graphs. Motion graphs. Creating a motion that follows a 2D path (Kovar, Gleicher & Pighin 02’). Creating a motion that follows a 2D path (Kovar, Gleicher & Pighin 02’). High level control (Lee et-al 02’). High level control (Lee et-al 02’). Hard and soft constraints (Arikan & Forsyth 02’). Hard and soft constraints (Arikan & Forsyth 02’). Problems: need a huge database. Problems: need a huge database.

11. Textural motion data Run cycle

12. Textural motion data Lie Down

13. Textural motion data High Wire

14. Construct a multi-resolution tree Low-pass. Low-pass. Sub-sample. Sub-sample.

15. Create an empty synthesis tree Build an empty synthesis tree Build an empty synthesis tree Finest level will contain the output. Finest level will contain the output.

16. Copy the constrained frames Copy constraints and a small neighborhood. Copy constraints and a small neighborhood. Copy the constraints ancestors. Copy the constraints ancestors.

17. Fill the gaps Search the input tree for the best matching frame in the corresponding level. Search the input tree for the best matching frame in the corresponding level.

18. Adaptive local smoothing Most frames are sequential. Most frames are sequential. Apply smoothing only where necessary. Apply smoothing only where necessary.

19. Frame representation Joint angle representation: Joint angle representation: Pro – changes keep skeleton structure. Pro – changes keep skeleton structure. Con – not good for comparing between frames. Con – not good for comparing between frames. 3D pose representation: 3D pose representation: Pro – good for calculating distance between frames. Pro – good for calculating distance between frames. Con – changing position alter the skeleton’s shape. Con – changing position alter the skeleton’s shape.

20. Multi-level frame neighborhood

21. Distance metric Use 3D pose representation. Use 3D pose representation. Compute distance between joint velocities. Compute distance between joint velocities. The metric: The metric:

22. Gap filling – searching for the best meeting point Synthesize frames alternating sides. Exhaustive search for best meeting point. Exhaustive search for best meeting point.

23. Gap filling – Acceleration

24. Gap filling – Finding a path in a graph Create a distance matrix for the coarsest level. Create a distance matrix for the coarsest level. Build a graph of frames. Build a graph of frames. Search for a path in the graph. Search for a path in the graph. In the finer levels – search between the sons of the chosen frames. In the finer levels – search between the sons of the chosen frames. Pro: fast, enables multiple meeting points. Pro: fast, enables multiple meeting points. Con: less control, lower quality. Con: less control, lower quality.

25. Results – Drunk walk (7 / 8 constraints, 37 / 43 sec. )

26. Results – High wire (6 constraints, 32 sec.)

27. Results – Cool walk (8 constraints, 30 sec)

28. Results - Ballet walk (9 constraints, 81 sec.)

29. Thank you