---
license: mit
tags:
  - diffusion
  - trajectory-generation
  - conditional-generation
  - pytorch
library_name: pytorch
pipeline_tag: other
---

# Sketch-Guided Trajectory Diffusion

A diffusion model for generating smooth and diverse trajectories conditioned on sparse sketch guidance.

This model explores sketch-conditioned trajectory simulation using denoising diffusion techniques. Given a coarse spatial sketch or trajectory prior, the model generates realistic trajectory samples that preserve the intended global structure while allowing stochastic local variation.

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

Code base:
Model - https://github.com/wezteoh/gameplay-trajectory-diffusion
Sketch canvas - https://github.com/wezteoh/gameplay-trajectory-canvas

## Overview

The model learns a conditional diffusion process over trajectory sequences:

- Encode partially observed trajectory guidance
- Add noise to trajectories during training
- Learn iterative denoising conditioned on sketches
- Sample plausible trajectories at inference time

Applications include:

- game AI movement simulation
- multi-agent gameplay strategy simulation
- synthetic behavior generation

---

## Model Details

### Inputs

- sparse trajectory sketches
- trajectory masks

### Outputs

- generated trajectory sequences

### Architecture

- diffusion transformer backbone adapted for spatiotemporal task
- DPM-solver / iterative DDPM-style sampling

---

## Usage

```python
python scripts/sample_trajectory_ddpm.py \
    --checkpoint ckpt_file_path \
    --num-samples 8 \
    --input-dir sketches_dir_path \
    --save-videos
```