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2.3. Motion Editing
Peters and O’Sullivan equally proposed a model based on the saliency maps previously
discussed [Peters and O’Sullivan, 2003]. They actually combined it with the stage theory
model of memory presented in [Peters and O’Sullivan, 2002].
Courty et al. also proposed a model based on saliency maps [Courty et al., 2003]. They
modeled the human perception process by using a saliency map based on geometric and
depth information. In order to do this, they combined a spatial frequencies feature map with
a depth feature map. They then applied this to a virtual character in order for it to perceive
its environment in a biologically plausible way.
On a different note, Peters and Itti conducted an experiment in which they tracked subjects’
gazes while they played computer games [Peters and Itti, 2006]. They tested various
heuristics to predict where the users would direct their attention. They compared outlierbased
heuristics and local heuristics. Their results showed that heuristics which detect outliers
from the global distribution of visual features were better predictors than the local ones.
They concluded that bottom-up image analysis could predict an important part of human
gaze targets in the case of video games.
Yu and Terzopoulos proposed a decision network framework to simulate how people
make decisions on what to attend to and on how to react [Yu and Terzopoulos, 2007]. Their
virtual characters are endowed with an intention generator, based on internal attributes and
memory. They receive perceptual data by querying the environment. This data comes under
the form of position, speed and orientation. They then decide on what to attend to depending
on their current intention and on possible abrupt visual onsets. Finally, they endow their
virtual characters with a memory system which allows them to remain consistent in their
behaviors and adapt to changes in the environment. This approach equally aims at animating
single characters or small groups of characters, but not large amounts of them, such as would
be seen in virtual crowds.
The approach we propose for character attention behavior synthesis resides on the automatic
detection of interest points based on bottom-up visual attention. Our method uses
character trajectories from pre-existing crowd animations to automatically determine the interest
points in a dynamic environment. Since it relies on trajectories only, it is generic, and
can be used with any kind of crowd animation engine. Moreover, it allows the generation of
attention behaviors for large crowds of characters. In a second step, we propose an alleviated
version of our method, directly integrated in a crowd engine. In this method, we determine
the interest points from user position, user’s interest position, and character positions. It also
allows the generation of attention behaviors for large crowds of characters in real-time.
2.3 Motion Editing
Motion editing, i.e. the modification of character movements, is also a vast domain which has
been worked on in profusion. A large category of methods relies on the skillful manipulation
of motion clips from a motion capture database by blending [Kovar and Gleicher, 2003]orby
defining motion graphs [Kovaretal., 2002a; Arikan and Forsyth, 2002; Lee et al., 2002a].
Due to the many possible configurations in attention behaviors, this would require a very
dense database in our case.
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