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https://github.com/agrimgupta92/sgan

Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
https://github.com/agrimgupta92/sgan

deep-learning generative-adversarial-network human-trajectory-prediction pytorch social-navigation trajectory-prediction

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Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018

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README 

        
# Social GAN



This is the code for the paper



**Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks**




Agrim Gupta,

Justin Johnson,

Fei-Fei Li,

Silvio Savarese,

Alexandre Alahi




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.










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”.





  




## 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/_.pt`: Contains 10 pretrained models for all five datasets. These models correspond to SGAN-20V-20 in Table 1.

- `sgan-p-models/_.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).