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Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

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This is the official implementation of the paper [STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits](https://aaai.org/ojs/index.php/AAAI/article/view/5490). Please use the following citation if you find our work uesful:

```
@inproceedings{bhattacharya2020step,
author = {Bhattacharya, Uttaran and Mittal, Trisha and Chandra, Rohan and Randhavane, Tanmay and Bera, Aniket and Manocha, Dinesh},
title = {STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits},
year = {2020},
publisher = {AAAI Press},
booktitle = {Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence},
pages = {1342–1350},
numpages = {9},
series = {AAAI’20}
}
```

We have also released the Emotion-Gait dataset with this code, which is available for download here: [https://go.umd.edu/emotion-gait](https://go.umd.edu/emotion-gait).

1. `generator_cvae` is the generator.
2. `classifier_stgcn_real_only` is the baseline classifier using only the real 342 gaits.
3. `classifier_stgcn_real_and_synth` is the baseline classifier using both real 342 and N synthetic gaits.
4. `clasifier_hybrid` is the hybrid classifier using both deep and physiologically-motivated features.
5. `compute_aff_features` consists of the set of scripts to compute the affective features from 16-joint pose sequences. Calling `main.py` with the correct data path computes the features, and save them in the `affectiveFeatures<f_type>.h5` file, where `f_type` is the desired type of features:

* `''` original data (default)
	* `4DCVAEGCN` data generated by the CVAE.

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