Neuranimation: Reactive Character Animations with Deep Neural Networks

The increasing need for more realistic animations has resulted in the implementation of various systems that try to overcome this issue by controlling the character at a base level based on complex techniques. In our work we are using a Phase Functioned Neural Network for generating the next pose of the character in real time while making a comparison with a modified version of the model. The current basic model lacks the ability of producing reactive animations with objects of their surrounding but only reacts to the terrain the character is standing on. Therefore, adding a layer of Rigs with Inverse Kinematics and Blending Trees will allow us to switch between actions depending on the object and adjust the character to fit properly. Our results showed that our proposal improves significantly previous results and that inverse kinematics is essential for this improvement.