Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
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
Last synced: 3 months ago
JSON representation
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
- Host: GitHub
- URL: https://github.com/agrimgupta92/sgan
- Owner: agrimgupta92
- License: mit
- Created: 2018-06-29T04:58:03.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-11-24T09:20:51.000Z (12 months ago)
- Last Synced: 2024-05-09T14:36:12.596Z (6 months ago)
- Topics: deep-learning, generative-adversarial-network, human-trajectory-prediction, pytorch, social-navigation, trajectory-prediction
- Language: Python
- Homepage:
- Size: 205 KB
- Stars: 796
- Watchers: 17
- Forks: 257
- Open Issues: 66
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-robotic-tooling - sgan - Socially Acceptable Trajectories with Generative Adversarial Networks. (Prediction / Vector Map)
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).