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

Human Pose Forecasting via Deep Markov Models