It does not happen so often that the work of our students get’s submitted to a conference. This time, not only was the work of Mathias submitted to a conference, it was also accepted for publication, and received the best student paper award!
Congratulations to Mathias’ for this great achievement!
References
2022
Neural Network-based Human Motion Smoother
Mathias
Bastholm
, Stella
Graßhof, and Sami
Brandt
In the International Conference on Pattern Recognition Applications and Methods (ICPRAM) , Feb 2022
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.
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