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This is the official implementation of the paper "Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents".

Home Page: https://gamma.umd.edu/t2g/

License: MIT License

Python 100.00%
affective-computing intelligent-agent virtual-agent gesture-generation text-processing

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text2gestures's Issues

Reproducing Pre-Trained Model Results

I'm running into some issues reproducing the results of this implementation.
Using the pre-trained model, everything works fine and the resulting gestures are convincing. Trying to reproduce these results by training a new model on the same dataset, however, has not yielded any results.
I have attempted using both the parameters described in the paper (lr=0.001, epochs=600) as well as those given here as they are to no success.
Resulting gestures are incoherent and the model does not linearly converge as I would have expected (for a 600 epoch session, the best mean loss was 0.27 at epoch 166/600).
I was wondering what parameters the pre-trained model was trained under, and whether they match up to those as they are set in the given main.py file.
Also, is this convergence behaviour as you would expect? Did you experience the same while training this model?
I have attached one of the output videos of epoch 510 for reference, as well as the log file of that 600 epoch training session.

000000.mp4

log.txt

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