Text-Guided Synthesis of Crowd Animation

Creating vivid crowd animations is core to immersive virtual environments in digital games. This work focuses on tackling the challenges of the crowd behavior generation problem. Existing approaches are labor-intensive, relying on practitioners to manually craft the complex behavior systems. We propose a machine learning approach to synthesize diversified dynamic crowd animation scenarios for a given environment based on a text description input. We first train two conditional diffusion models that generate text-guided agent distribution fields and velocity fields. Assisted by local navigation algorithms, the fields are then used to control multiple groups of agents. We further employ Large-Language Model (LLM) to canonicalize the general script into a structured sentence for more stable training and better scalability. To train our diffusion models, we devise a constructive method to generate random environments and crowd animations. We show that our trained diffusion models can generate crowd animations for both unseen environments and novel scenario descriptions. Our method paves the way towards automatic generating of crowd behaviors for virtual environments. Code and data for this paper are available at: https://github.com/MLZG/Text-Crowd.git.