Intention-based Long-Term Human Motion Anticipation
Julian Tanke, Chintan Zaveri and Jürgen Gall
Recently, a few works have been proposed to model the uncertainty of the future human motion. These works do not forecast a single sequence but multiple sequences for the same observation. While these works focused on increasing the diversity, this work focuses on keeping a high quality of the forecast sequences even for very long time horizons of up to 30 seconds. In order to achieve this goal, we propose to forecast the intention of the person ahead of time. This has the advantage that the generated human motion remains goal oriented and that the motion transitions between two actions are smooth and highly realistic. We furthermore propose a new quality metric for evaluation that correlates better with human perception than other metrics. The results and a user study show that our approach forecasts multiple sequences that are more plausible compared to the state-of-the-art.