DyST: Towards Dynamic Neural Scene Representations on Real-World Videos
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866909137507450880 |
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| author | Seitzer, Maximilian van Steenkiste, Sjoerd Kipf, Thomas Greff, Klaus Sajjadi, Mehdi S. M. |
| author_facet | Seitzer, Maximilian van Steenkiste, Sjoerd Kipf, Thomas Greff, Klaus Sajjadi, Mehdi S. M. |
| contents | Visual understanding of the world goes beyond the semantics and flat structure of individual images. In this work, we aim to capture both the 3D structure and dynamics of real-world scenes from monocular real-world videos. Our Dynamic Scene Transformer (DyST) model leverages recent work in neural scene representation to learn a latent decomposition of monocular real-world videos into scene content, per-view scene dynamics, and camera pose. This separation is achieved through a novel co-training scheme on monocular videos and our new synthetic dataset DySO. DyST learns tangible latent representations for dynamic scenes that enable view generation with separate control over the camera and the content of the scene. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_06020 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DyST: Towards Dynamic Neural Scene Representations on Real-World Videos Seitzer, Maximilian van Steenkiste, Sjoerd Kipf, Thomas Greff, Klaus Sajjadi, Mehdi S. M. Computer Vision and Pattern Recognition Artificial Intelligence Graphics Machine Learning Robotics Visual understanding of the world goes beyond the semantics and flat structure of individual images. In this work, we aim to capture both the 3D structure and dynamics of real-world scenes from monocular real-world videos. Our Dynamic Scene Transformer (DyST) model leverages recent work in neural scene representation to learn a latent decomposition of monocular real-world videos into scene content, per-view scene dynamics, and camera pose. This separation is achieved through a novel co-training scheme on monocular videos and our new synthetic dataset DySO. DyST learns tangible latent representations for dynamic scenes that enable view generation with separate control over the camera and the content of the scene. |
| title | DyST: Towards Dynamic Neural Scene Representations on Real-World Videos |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics Machine Learning Robotics |
| url | https://arxiv.org/abs/2310.06020 |