# Social GAN

This is the code for the paper

**<a href="https://arxiv.org/abs/1803.10892">Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks</a>**
<br>
<a href="http://web.stanford.edu/~agrim/">Agrim Gupta</a>,
<a href="http://cs.stanford.edu/people/jcjohns/">Justin Johnson</a>,
<a href="http://vision.stanford.edu/feifeili/">Fei-Fei Li</a>,
<a href="http://cvgl.stanford.edu/silvio/">Silvio Savarese</a>,
<a href="http://web.stanford.edu/~alahi/">Alexandre Alahi</a>
<br>
Presented at [CVPR 2018](http://cvpr2018.thecvf.com/)

Human motion is interpersonal, multimodal and follows social conventions. In this paper, we tackle this problem by combining tools from sequence prediction and generative adversarial networks: a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across
people.

Below we show an examples of socially acceptable predictions made by our model in complex scenarios. Each person is denoted by a different color. We denote observed trajectory by dots and predicted trajectory by stars.
<div align='center'>
<img src="images/2.gif"></img>
<img src="images/3.gif"></img>
</div>

If you find this code useful in your research then please cite
```
@inproceedings{gupta2018social,
  title={Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks},
  author={Gupta, Agrim and Johnson, Justin and Fei-Fei, Li and Savarese, Silvio and Alahi, Alexandre},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  number={CONF},
  year={2018}
}
```

## Model
Our model consists of three key components: Generator (G), Pooling Module (PM) and Discriminator (D). G is based on encoder-decoder framework where we link the hidden states of encoder and decoder via PM. G takes as input trajectories of all people involved in a scene and outputs corresponding predicted trajectories. D inputs the entire sequence comprising both input trajectory and future prediction and classifies them as “real/fake”.

<div align='center'>
  <img src='images/model.png' width='1000px'>
</div>

## Setup
All code was developed and tested on Ubuntu 16.04 with Python 3.5 and PyTorch 0.4.

You can setup a virtual environment to run the code like this:

```bash
python3 -m venv env               # Create a virtual environment
source env/bin/activate           # Activate virtual environment
pip install -r requirements.txt   # Install dependencies
echo $PWD > env/lib/python3.5/site-packages/sgan.pth  # Add current directory to python path
# Work for a while ...
deactivate  # Exit virtual environment
```

## Pretrained Models
You can download pretrained models by running the script `bash scripts/download_models.sh`. This will download the following models:

- `sgan-models/<dataset_name>_<pred_len>.pt`: Contains 10 pretrained models for all five datasets. These models correspond to SGAN-20V-20 in Table 1.
- `sgan-p-models/<dataset_name>_<pred_len>.pt`: Contains 10 pretrained models for all five datasets. These models correspond to SGAN-20VP-20 in Table 1.

Please refer to [Model Zoo](MODEL_ZOO.md) for results.

## Running Models
You can use the script `scripts/evaluate_model.py` to easily run any of the pretrained models on any of the datsets. For example you can replicate the Table 1 results for all datasets for SGAN-20V-20 like this:

```bash
python scripts/evaluate_model.py \
  --model_path models/sgan-models
```

## Training new models
Instructions for training new models can be [found here](TRAINING.md).