Intention-based Long-Term Human Motion Anticipation


Julian Tanke, Chintan Zaveri and J├╝rgen 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.

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  Important Dates

All deadlines are 23:59 Pacific Time (PT). No extensions will be granted.

Paper registration July 23 30, 2021
Paper submission July 30, 2021
Supplementary August 8, 2021
Tutorial submission August 15, 2021
Tutorial notification August 31, 2021
Rebuttal period September 16-22, 2021
Paper notification October 1, 2021
Camera ready October 15, 2021
Demo submission July 30 Nov 15, 2021
Demo notification Oct 1 Nov 19, 2021
Tutorial November 30, 2021
Main conference December 1-3, 2021