Neural Network-Based Human Motion Smoother

Recording real life human motion as a skinned mesh animation with an acceptable quality is usually difficult.
Even though recent advances in pose estimation have enabled motion capture from off-the-shelf webcams, the low quality makes it infeasible for use in production quality animation.
This work proposes to use recent advances in the prediction of human motion through neural networks to augment low quality human motion, in an effort to bridge the gap between cheap recording methods and high quality recording.
First, a model, competitive with prior work in short-term human motion prediction, is constructed.
Then, the model is trained to clean up motion from two low quality input sources, mimicking a real world scenario of recording human motion through two webcams.
Experiments on simulated data show that the model is capable of significantly reducing noise, and it opens the way for future work to test the model on annotated data.