| --- |
| license: mit |
| tags: |
| - diffusion |
| - trajectory-generation |
| - conditional-generation |
| - pytorch |
| library_name: pytorch |
| pipeline_tag: other |
| --- |
| |
| # Sketch-Guided Trajectory Diffusion |
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| A diffusion model for generating smooth and diverse trajectories conditioned on sparse sketch guidance. |
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| 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. |
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| Blog post: |
| https://wezteoh.github.io/posts/diffusion-for-sketch-guided-trajectory-simulation/ |
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| Code base: |
| Model - https://github.com/wezteoh/gameplay-trajectory-diffusion |
| Sketch canvas - https://github.com/wezteoh/gameplay-trajectory-canvas |
|
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| ## Overview |
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| The model learns a conditional diffusion process over trajectory sequences: |
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| - Encode partially observed trajectory guidance |
| - Add noise to trajectories during training |
| - Learn iterative denoising conditioned on sketches |
| - Sample plausible trajectories at inference time |
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| Applications include: |
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| - game AI movement simulation |
| - multi-agent gameplay strategy simulation |
| - synthetic behavior generation |
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| --- |
|
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| ## Model Details |
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| ### Inputs |
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| - sparse trajectory sketches |
| - trajectory masks |
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| ### Outputs |
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| - generated trajectory sequences |
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| ### Architecture |
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| - diffusion transformer backbone adapted for spatiotemporal task |
| - DPM-solver / iterative DDPM-style sampling |
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| --- |
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| ## Usage |
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| ```python |
| python scripts/sample_trajectory_ddpm.py \ |
| --checkpoint ckpt_file_path \ |
| --num-samples 8 \ |
| --input-dir sketches_dir_path \ |
| --save-videos |
| ``` |
