  Modeling crowds has many important applications in games and computer
animation. Inspired by the emergent following effect in real-life crowd
scenarios, in this work, we develop a method for implicitly grouping moving
agents. We achieve this by analyzing local information around each agent and
rotating its preferred velocity accordingly. Each agent could automatically
form an implicit group with its neighboring agents that have similar
directions. In contrast to an explicit group, there are no strict boundaries
for an implicit group. If an agent's direction deviates from its group as a
result of positional changes, it will autonomously exit the group or join
another implicitly formed neighboring group. This implicit grouping is
autonomously emergent among agents rather than deliberately controlled by the
algorithm. The proposed method is compared with many crowd simulation models,
and the experimental results indicate that our approach achieves the lowest
congestion levels in some classic scenarios. In addition, we demonstrate that
adjusting the preferred velocity of agents can actually reduce the
dissimilarity between their actual velocity and the original preferred
velocity. Our work is available online.
