L4P: Towards Unified Low-Level 4D Vision Perception
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911188255768576 |
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| author | Badki, Abhishek Su, Hang Wen, Bowen Gallo, Orazio |
| author_facet | Badki, Abhishek Su, Hang Wen, Bowen Gallo, Orazio |
| contents | The spatio-temporal relationship between the pixels of a video carries critical information for low-level 4D perception tasks. A single model that reasons about it should be able to solve several such tasks well. Yet, most state-of-the-art methods rely on architectures specialized for the task at hand. We present L4P, a feedforward, general-purpose architecture that solves low-level 4D perception tasks in a unified framework. L4P leverages a pre-trained ViT-based video encoder and combines it with per-task heads that are lightweight and therefore do not require extensive training. Despite its general and feedforward formulation, our method is competitive with existing specialized methods on both dense tasks, such as depth or optical flow estimation, and sparse tasks, such as 2D/3D tracking. Moreover, it solves all tasks at once in a time comparable to that of single-task methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13078 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | L4P: Towards Unified Low-Level 4D Vision Perception Badki, Abhishek Su, Hang Wen, Bowen Gallo, Orazio Computer Vision and Pattern Recognition The spatio-temporal relationship between the pixels of a video carries critical information for low-level 4D perception tasks. A single model that reasons about it should be able to solve several such tasks well. Yet, most state-of-the-art methods rely on architectures specialized for the task at hand. We present L4P, a feedforward, general-purpose architecture that solves low-level 4D perception tasks in a unified framework. L4P leverages a pre-trained ViT-based video encoder and combines it with per-task heads that are lightweight and therefore do not require extensive training. Despite its general and feedforward formulation, our method is competitive with existing specialized methods on both dense tasks, such as depth or optical flow estimation, and sparse tasks, such as 2D/3D tracking. Moreover, it solves all tasks at once in a time comparable to that of single-task methods. |
| title | L4P: Towards Unified Low-Level 4D Vision Perception |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.13078 |