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SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen Cameras

4 Apr 2020arXiv:2004.02022archive 2025-07-28

Junwei Liang, Lu Jiang, Alexander Hauptmann

This paper focuses on the problem of predicting future trajectories of people in unseen scenarios and camera views. We propose a method to efficiently utilize multi-view 3D simulation data for training. Our approach finds the hardest camera view to mix up with adversarial data from the original camera view in training, thus enabling the model to learn robust representations that can generalize to unseen camera views. We refer to our method as SimAug. We show that SimAug achieves best results on three out-of-domain real-world benchmarks, as well as getting state-of-the-art in the Stanford Drone and the VIRAT/ActEV dataset with in-domain training data. We will release our models and code.

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Code

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Tasks

Adversarial Attack Adversarial Defense Pedestrian Trajectory Prediction Trajectory Forecasting Trajectory Prediction

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Trajectory Prediction	ActEV	SimAug	ADE-8/12	17.96	#2 of 4	Archive leaderboard	report
Trajectory Prediction	ActEV	SimAug	FDE-8/12	34.68	#2 of 4	Archive leaderboard	report
Trajectory Prediction	Stanford Drone	SimAug	ADE-8/12 @K = 20	10.27	#10 of 24	Archive leaderboard	report
Trajectory Prediction	Stanford Drone	SimAug	FDE-8/12 @K= 20	19.71	#10 of 24	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

Introduced by this paper: SimAug

SimAug

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