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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.07940 |
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| _version_ | 1866915949752352768 |
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| author | Luo, Rundong Wallingford, Matthew Farhadi, Ali Snavely, Noah Ma, Wei-Chiu |
| author_facet | Luo, Rundong Wallingford, Matthew Farhadi, Ali Snavely, Noah Ma, Wei-Chiu |
| contents | 360° videos have emerged as a promising medium to represent our dynamic visual world. Compared to the "tunnel vision" of standard cameras, their borderless field of view offers a more complete perspective of our surroundings. While existing video models excel at producing standard videos, their ability to generate full panoramic videos remains elusive. In this paper, we investigate the task of video-to-360° generation: given a perspective video as input, our goal is to generate a full panoramic video that is consistent with the original video. Unlike conventional video generation tasks, the output's field of view is significantly larger, and the model is required to have a deep understanding of both the spatial layout of the scene and the dynamics of objects to maintain spatio-temporal consistency. To address these challenges, we first leverage the abundant 360° videos available online and develop a high-quality data filtering pipeline to curate pairwise training data. We then carefully design a series of geometry- and motion-aware operations to facilitate the learning process and improve the quality of 360° video generation. Experimental results demonstrate that our model can generate realistic and coherent 360° videos from in-the-wild perspective video. In addition, we showcase its potential applications, including video stabilization, camera viewpoint control, and interactive visual question answering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07940 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond the Frame: Generating 360 Panoramic Videos from Perspective Videos Luo, Rundong Wallingford, Matthew Farhadi, Ali Snavely, Noah Ma, Wei-Chiu Computer Vision and Pattern Recognition 360° videos have emerged as a promising medium to represent our dynamic visual world. Compared to the "tunnel vision" of standard cameras, their borderless field of view offers a more complete perspective of our surroundings. While existing video models excel at producing standard videos, their ability to generate full panoramic videos remains elusive. In this paper, we investigate the task of video-to-360° generation: given a perspective video as input, our goal is to generate a full panoramic video that is consistent with the original video. Unlike conventional video generation tasks, the output's field of view is significantly larger, and the model is required to have a deep understanding of both the spatial layout of the scene and the dynamics of objects to maintain spatio-temporal consistency. To address these challenges, we first leverage the abundant 360° videos available online and develop a high-quality data filtering pipeline to curate pairwise training data. We then carefully design a series of geometry- and motion-aware operations to facilitate the learning process and improve the quality of 360° video generation. Experimental results demonstrate that our model can generate realistic and coherent 360° videos from in-the-wild perspective video. In addition, we showcase its potential applications, including video stabilization, camera viewpoint control, and interactive visual question answering. |
| title | Beyond the Frame: Generating 360 Panoramic Videos from Perspective Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.07940 |