https://github.com/stanfordasl/nhumanmodeling
Contains the code for "Generative Modeling of Multimodal Multi-Human Behavior" by Boris Ivanovic, Edward Schmerling, Karen Leung, and Marco Pavone.
https://github.com/stanfordasl/nhumanmodeling
deep-learning human-robot-interaction multi-agent-modeling trajectory-prediction
Last synced: 4 months ago
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Contains the code for "Generative Modeling of Multimodal Multi-Human Behavior" by Boris Ivanovic, Edward Schmerling, Karen Leung, and Marco Pavone.
- Host: GitHub
- URL: https://github.com/stanfordasl/nhumanmodeling
- Owner: StanfordASL
- License: mit
- Created: 2018-03-03T00:50:38.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-02-10T01:11:51.000Z (over 7 years ago)
- Last Synced: 2025-05-12T20:19:51.690Z (about 1 year ago)
- Topics: deep-learning, human-robot-interaction, multi-agent-modeling, trajectory-prediction
- Language: Jupyter Notebook
- Homepage:
- Size: 853 KB
- Stars: 16
- Watchers: 20
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
**UPDATE**: There is a newer and much faster version of this codebase implemented in PyTorch! Take a look **[here](https://github.com/StanfordASL/DynSTGModeling)**!
# N-Human Modeling
This repository contains the code for [Generative Modeling of Multimodal Multi-Human Behavior](https://arxiv.org/abs/1803.02015) by Boris Ivanovic, Edward Schmerling, Karen Leung, and Marco Pavone.
**Note**: We use [Git LFS](https://git-lfs.github.com) to version large files (such as model checkpoints and data).
## Installation ##
First, we'll create a conda environment to hold the dependencies.
```
conda create --name modeling python=2.7 -y
source activate modeling
pip install -r requirements.txt
```
Then, since this project uses IPython notebooks, we'll install this conda environment as a kernel.
```
python -m ipykernel install --user --name modeling --display-name "Python 2.7 (NHumanModeling)"
```
Now, you can start a Jupyter session and view/run all the notebooks with
```
jupyter notebook
```
When you're done, don't forget to deactivate the conda environment with
```
source deactivate
```
## Datasets ##
The preprocessed datasets are available in this repository, under `data/` folders (e.g. `nba-dataset/data/`).
If you want the *original* traffic weaving or NBA datasets, I obtained them from here: [Traffic Weaving Dataset](https://github.com/StanfordASL/TrafficWeavingCVAE) and [NBA Dataset](https://github.com/linouk23/NBA-Player-Movements).