Papers › Intention-based Long-Term Human Motion Anticipation

Intention-based Long-Term Human Motion Anticipation

1 Dec 20213DV 2021 12archive 2025-07-28

Julian Tanke, Chintan Zaveri, Juergen 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 in- creasing 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 mo- tion remains goal oriented and that the motion transitions between two actions are smooth and highly realistic. We furthermore propose a new quality score for evaluation that correlates better with human perception than other metrics. The results and a user study show that our approach fore- casts multiple sequences that are more plausible compared to the state-of-the-art.

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Code

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Tasks

DiversityHuman Pose Forecasting

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Human Pose Forecasting Human3.6M Forecast LSTM APD 3070 #17 of 33 Archive leaderboard report
Human Pose Forecasting Human3.6M Forecast LSTM Average MPJPE (mm) @ 1000 ms 149.2 #17 of 33 Archive leaderboard report
Human Pose Forecasting Human3.6M Forecast LSTM Average MPJPE (mm) @ 400ms 80.8 #17 of 33 Archive leaderboard report
Human Pose Forecasting Human3.6M Forecast LSTM Average NDMS at 4s 0.465 #17 of 33 Archive leaderboard report
Human Pose Forecasting Human3.6M Forecast LSTM MAR, walking, 400ms 0.91 #17 of 33 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

LSTMSigmoid ActivationTanh Activation

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