Papers › Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, Alexandre Alahi
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments. This is challenging because human motion is inherently multimodal: given a history of human motion paths, there are many socially plausible ways that people could move in the future. We tackle this problem by combining tools from sequence prediction and generative adversarial networks: a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people. We predict socially plausible futures by training adversarially against a recurrent discriminator, and encourage diverse predictions with a novel variety loss. Through experiments on several datasets we demonstrate that our approach outperforms prior work in terms of accuracy, variety, collision avoidance, and computational complexity.
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Code
Syntology Ran 4 of 10 code samples harvested from 3 repositories linked to this paper; 6 have no recorded run. Of those that ran: 3 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
10 samples harvested; 4 ran; 0 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Trajectory Prediction | ETH | Social-GAN | Avg AMD/AMV 8/12 | 1.42 | #4 of 5 | Archive leaderboard | report |
| Trajectory Prediction | Stanford Drone | Social GAN | ADE (8/12) @K=5 | 27.25 | #18 of 24 | Archive leaderboard | report |
| Trajectory Prediction | Stanford Drone | Social GAN | ADE-8/12 @K = 20 | 27.23 | #18 of 24 | Archive leaderboard | report |
| Trajectory Prediction | Stanford Drone | Social GAN | FDE(8/12) @K=5 | 41.44 | #18 of 24 | Archive leaderboard | report |
| Trajectory Prediction | Stanford Drone | Social GAN | FDE-8/12 @K= 20 | 41.44 | #18 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.
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