Context-free Self-Conditioned GAN for Trajectory Forecasting
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911499293818880 |
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| author | de Almeida, Tiago Rodrigues Maestro, Eduardo Gutierrez Mozos, Oscar Martinez |
| author_facet | de Almeida, Tiago Rodrigues Maestro, Eduardo Gutierrez Mozos, Oscar Martinez |
| contents | In this paper, we present a context-free unsupervised approach based on a self-conditioned GAN to learn different modes from 2D trajectories. Our intuition is that each mode indicates a different behavioral moving pattern in the discriminator's feature space. We apply this approach to the problem of trajectory forecasting. We present three different training settings based on self-conditioned GAN, which produce better forecasters. We test our method in two data sets: human motion and road agents. Experimental results show that our approach outperforms previous context-free methods in the least representative supervised labels while performing well in the remaining labels. In addition, our approach outperforms globally in human motion, while performing well in road agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08658 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Context-free Self-Conditioned GAN for Trajectory Forecasting de Almeida, Tiago Rodrigues Maestro, Eduardo Gutierrez Mozos, Oscar Martinez Machine Learning In this paper, we present a context-free unsupervised approach based on a self-conditioned GAN to learn different modes from 2D trajectories. Our intuition is that each mode indicates a different behavioral moving pattern in the discriminator's feature space. We apply this approach to the problem of trajectory forecasting. We present three different training settings based on self-conditioned GAN, which produce better forecasters. We test our method in two data sets: human motion and road agents. Experimental results show that our approach outperforms previous context-free methods in the least representative supervised labels while performing well in the remaining labels. In addition, our approach outperforms globally in human motion, while performing well in road agents. |
| title | Context-free Self-Conditioned GAN for Trajectory Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.08658 |