Towards Practical Automated Motion Synthesis

1
Towards Practical Automated Motion Synthesis

• Auslander, Fukunaga, Partovi, Christensen, Hsu,
  Reiss, Shuman, Marks, Ngo

2
Some New Goals

• Improve initial algorithm (Marks and Ngo)
• improve efficiency
• allow user to influence motions
• Composite motion synthesis
• using a predefined library of controllers
• concatenating controllers is complex
• Going from 2D to 3D

3
Improving efficiency(Serial Search Algorithm)
Initialize a set of random motion
controllers Evaluate each motion controller in
the set for i 1 to number_of_iterations for
each individual controller in the set do Mutate
(i.e., reinitialize or perturb) the
controller Evaluate the new controller if the
new controller is better that the old one
then Replace the old controller with the new
one end for if (i mod reseed_interval) 0
then Rank order the set of controllers Replace
bottom 50 of the set with top 50 end if end
for

• Works like multiple hill climbers in parallel
• Reseeding refocuses the search

4
Effects of New Algorithm

• Greater speed achieved
• 3-6 minutes on a single DEC work station
• Greater focus of search
• entire space of trajectories no longer considered
• only space of possible motion controllers
  searched
• Important to note
• Crossover breeding no longer used!
• Mutations drive change

5
What Influences the Motion

• Factors influencing motion
• Primary fitness terms
• term given the most weight in fitness function
• usually rewards for net displacement
• but artist cannot encourage specific tactics
• e.g., roll instead of walking
• Sense-based motion controllers
• Behavior based on sense variables alone
• No notion of repetitive behavior

6
Influencing Minor Traits

• Introduce secondary fitness terms
• terms that encourage certain tactics
• Using time-based motion controllers
• make time-taken a factor in determining moves

7
Secondary Fitness Terms

• Term in with less weight than primary term
• determines minor characteristics of motion

Max_cm_height - maximum height of center of mass
during motion slip_sum - Distance traveled by
body parts in continuous contact with
ground rotations - Number of full-body rotations
during motion

• Terms encourage certain trait in motion
• e.g., shuffling, gyrations, hopping

8
Time-based Motion Controllers

• All sense variables reduced to one time
• easier to code
• easier to compute one
• Simulation broken into time segments
• Apply SR rules within segments
• Cannot perform moves that take longer than given
  time segment allows
• Repeat segments
• Size of segments determines motion
• can control (a)periodicity of motions
• e.g, Shuffling vs. tumbling of Beryl Biped

9
Composite Motion Synthesis

• Allowing animator to concatenate motion
  controllers using an animation editor
• Easier than earlier approaches
• using pre-made motion controllers
• no SC problems to solve in animation editor
• Major challenges exist
• starting a motion controller from an initial
  configuration it was not designed for

10
Different Starting Configurations

• Simple animation editor created
• Sense-based BSR controllers are robust
• only similar initial configuration needed for
  simple motions
• human selection of alternate transition points
• requires solid understanding of controllers
  available
• care needed - BSR controllers are only so robust
• tedious work
• Automated transition point selection needed

11
Automated Transition Point Selection

• Three techniques used
• Create more robust motion controllers
• randomly perturb initial configurations of
  articulated figures during each iteration of
  motion synthesis
• Using a composite-motion scripting language
• quantifying each phase of composite motion
• interpreter translates characteristics into
  fitness function for optimization
• Heuristic search for best transition pt.
• Greedy best first search for local optimum for
  random initial set of transition points.

12
Going from 2D to 3D

• Increased complexity
• Dimensionality of controller space doubles
• Much of controller search space now causes loss
  of balance
• Type of controllers used
• time-based
• performed better than expected
• sense-based

13
Time-based Controllers in 3D

• worked faster than sense-based controllers
• easier to solve smaller, easier time problems
• proved fitter - faster learning curve
• certain limitations surface
• unstable 3D figures prove hard to implement
• harder to concatenate time-based controllers

14
Sense-based Controllers in 3D

• Take longer, but show no limitations
• Increased complexity
• All vectors now 3D
• New rules desirable in 3D simulation
• Better mutation algorithm needed
• Better initial setup needed

15
Better Mutation Algorithm

• Greater complexity, larger controller space
• Better heuristics needed in mutations
• purely random mutations unlikely to have an
  effect in incredibly large controller space
• aggressive relevence heuristics needed to create
  random regions that guarantee an effect
• new heuristics demand that sensitive region now
  share some points with old sense space trajectory

16
Better initial setup needed

• Three rounds of mutations conducted on initial
  set of motion controllers
• Low pass filter on values of physical senses
• remove noise
• remove high-frequency variation