KP-RNN: A Deep Learning Pipeline for Human Motion Prediction and Synthesis of Performance Art. (arXiv:2210.04366v3 [cs.CV] UPDATED)

Digitally synthesizing human motion is an inherently complex process, which
can create obstacles in application areas such as virtual reality. We offer a
new approach for predicting human motion, KP-RNN, a neural network which can
integrate easily with existing image processing and generation pipelines. We
utilize a new human motion dataset of performance art, Take The Lead, as well
as the motion generation pipeline, the Everybody Dance Now system, to
demonstrate the effectiveness of KP-RNN's motion predictions. We have found …

application art arxiv deep learning human image image processing network neural network performance performance art pipeline prediction process processing reality rnn synthesis virtual virtual reality