  Human pose forecasting is an important problem in computer vision with
applications to human-robot interaction, visual surveillance, and autonomous
driving. Usually, forecasting algorithms use 3D skeleton sequences and are
trained to forecast for a few milliseconds into the future. Long-range
forecasting is challenging due to the difficulty of estimating how long a
person continues an activity. To this end, our contributions are threefold: (i)
we propose a generative framework for poses using variational autoencoders
based on Deep Markov Models (DMMs); (ii) we evaluate our pose forecasts using a
pose-based action classifier, which we argue better reflects the subjective
quality of pose forecasts than distance in coordinate space; (iii) last, for
evaluation of the new model, we introduce a 480,000-frame video dataset called
Ikea Furniture Assembly (Ikea FA), which depicts humans repeatedly assembling
and disassembling furniture. We demonstrate promising results for our approach
on both Ikea FA and the existing NTU RGB+D dataset.
