Title: Automated Construction of Parameterized Motions
1Automated Construction of Parameterized Motions
- Lucas Kovar
- Michael Gleicher
- University of Wisconsin-Madison
2Parameterized Motion
Blend (interpolate) captured motions to make new
ones
Map blend weights to motion features for
intuitive control
(Wiley and Hahn 97 Rose et al. 98,01 Park et
al.02)
3Adapting Parameterized Motion to Large Data Sets
- Previous work used manual blending methods on
small, contrived data sets.
- We introduce automated tools that simplify
working with larger, more general data sets - Automatically locate examples
- Automatic blending (discussed previously)
- Accurate, efficient, and stable parameterization
Inputs one example feature of interest
4Outline
- Finding example motions
- Parameterizing blends
- Results
5Outline
- Finding example motions
- Parameterizing blends
- Results
6Finding Motions
- Example motions are buried in longer motions.
ready stance
punch
dodge
punch
Strategy search for motion segments similar to a
query.
7Related Work Searching Time Series Databases
Goal find data segments (matches) whose
distance to query is lt e.
- (Faloutsos et al. 94) place low-dimensional
approximation in spatial hierarchy - (Cardle et al. 03, Liu et al. 03 Keogh et al.
04) motion data
Confuses unrelated motions with distinct variants
8Logically Similar ? Numerically Similar!
9Our Search Strategy
Find close matches and use as new queries.
Precompute potential matches to gain efficiency.
10Determining Numerical Similarity
Factor out timing with a time alignment (just as
with registration curves).
Time alignment
Segment 1
,
Segment 1
Segment 2
Segment 2
Compare average distance between corresponding
frames with threshold.
11Precomputing Matches Intuitions
Any subset of an optimal path is optimal.
Motion 1
Motion 2
Optimal paths are redundant under endpoint
perturbation.
12Match Webs
Compute a grid of frame distances and find long,
locally optimal paths.
Motion 1
Represents all possibly similar segments.
Motion 2
13Searching With Match Webs
At run time, intersect queries with the match web
to find matches.
Motion 1
Motion 2
14Search Results
- 37,000 frame data set with 10 kinds of motion.
- 50 min. to create match web, 21MB on disk
- All searches (up to 97 queries) in 0.5s
- Manual verification of accuracy
- Can not discern meaning of motions!
picking up
putting back
15Outline
- Finding example motions
- Parameterizing blends
- Results
16Natural Parameterizations
Blend weights offer a poor parameterization.
We need more natural parameters.
parameters
motion
reaching
hand position at apex
turning
change in hip orientation
jumping
max height of center of mass
17From Parameters to Blend Weights
It is easy to map blend weights to parameters.
blend weights
blend
parameters
But we want !
This has no closed-form representation.
18Building Parameterizations
Can approximate from samples
with scattered data interpolation (Rose et al
98).
Accuracy create blends to generate new samples.
(see also Rose et al 01)
19Sampling
Require sampled weights to be nearly convex
and
for
Sample blend weights only for subsets of nearby
motions.
20Scattered Data Interpolation
- Previous work uses an RBF interpolation
method that does not constrain blend weights. - (Rose et al 98,01) (Park et al. 02)
K-nearest-neighbor interpolation is (almost) and
ensures blend weights are nearly convex.
21Outline
- Finding example motions
- Parameterizing blends
- Results
22Results
23Discussion
- Parameterized motions make it easy to synthesize
and edit motion. - We want lots of them, so we need tools that
simplify their construction - Automated extraction of examples
- Efficient and accurate parameterization that
respects boundaries implied by data