Diffusion models for sketch-guided trajectory simulation [R]

Blog post: https://wezteoh.github.io/posts/diffusion-for-sketch-guided-trajectory-simulation/

During NBA games, coaches often sketch attacking plays on a whiteboard and mentally simulate how teammates and defenders might react.

In this project, I explored using diffusion models for controllable basketball trajectory simulation. Instead of only forecasting future trajectories, the model generates gameplay conditioned on partial “sketches” of player movement instructions.

One interesting aspect is that diffusion models refine all player trajectories jointly, which makes sketch-conditioned simulation feel more natural compared to autoregressive generation.

I wrote up the methodology, experiments, and implementation details in the link above. Code and model are fully open sourced as well. Curious to hear thoughts from others working on generative modeling, trajectory prediction, or sports analytics.