Deep Generative Modeling of Character Animation

The goal of this research project is to develop novel techniques to solve different tasks for character animation using deep neural networks and generative modeling. Namely, we wish propose a novel approach for transitions generation, in which clips of character animation can be linked together with a novel clip. This transition will be generated by a specifically designed recurrent neural network that should make use of recent advances in adversarial learning in order to produce realistic animations. We also want to tackle the problem of key-frame interpolation, where we want to improve the current techniques of motion smoothing and interpolations by learning a dense pose manifold that takes into account complete character configurations in order to only produce valid poses while interpolating.

Intern: 
Félix Gingras Harvey
Faculty Supervisor: 
Christopher Pal
Project Year: 
2018
Province: 
Quebec
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