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Code for Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

Code for Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

Social GAN

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

Social-GANV

Social-GAN

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”.

Social-GANvs

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:

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 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:

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

GitHub