Speech_driven_gesture_generation_with_autoencoder
Advanced-Deep-Learning-with-Keras
| Speech_driven_gesture_generation_with_autoencoder | Advanced-Deep-Learning-with-Keras | |
|---|---|---|
| 4 | 1 | |
| 103 | 1,716 | |
| 1.0% | 0.2% | |
| 0.0 | 0.0 | |
| 10 months ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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Speech_driven_gesture_generation_with_autoencoder
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[R] Moving Fast and Slow: Analysis of Representations and Post-Processing in Speech-Driven Automatic Gesture Generation. Code and demo available
Project page: https://svito-zar.github.io/audio2gestures/
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[R] Moving Fast and Slow: Analysis of Representations and Post-Processing in Speech-Driven Automatic Gesture Generation. Code and dataset available
Project page: https://svito-zar.github.io/audio2gestures/
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[R] Moving Fast and Slow: Analysis of Representations and Post-Processing in Speech-Driven Automatic Gesture Generation. Code available
Code: https://github.com/GestureGeneration/Speech_driven_gesture_generation_with_autoencoder
Advanced-Deep-Learning-with-Keras
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Cannot understand how REINFORCE model is trained
I have understood the concept of REINFORCE algorithm and what policy gradient is. However, when I see the code published by PacktPublishing, I was stuck with it.
What are some alternatives?
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