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

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 22.04 with Python 3.10 and torch

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

conda create -n test python=3.10 -y               # Create a virtual environment
conda activate test                               # Activate virtual environment
# Work for a while ...
conda deactivate  # Exit virtual environment

clone repo and download files

git clone https://github.com/bharath5673/Social-GAN.git
cd Social-GAN
pip install -r requirements.txt   # Install dependencies
sh scripts/download_data.sh
sh scripts/download_models.sh

Please refer to Model Zoo 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:

cd scripts
sh run_eval.sh

Training new models

cd scripts
sh run_traj.sh

Instructions for training new models can be found here.

bharath5673 / Social-GAN