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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2401.17509 |
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| _version_ | 1866910312042594304 |
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| author | Bai, Chen Shao, Zeman Zhang, Guoxiang Liang, Di Yang, Jie Zhang, Zhuorui Guo, Yujian Zhong, Chengzhang Qiu, Yiqiao Wang, Zhendong Guan, Yichen Zheng, Xiaoyin Wang, Tao Lu, Cheng |
| author_facet | Bai, Chen Shao, Zeman Zhang, Guoxiang Liang, Di Yang, Jie Zhang, Zhuorui Guo, Yujian Zhong, Chengzhang Qiu, Yiqiao Wang, Zhendong Guan, Yichen Zheng, Xiaoyin Wang, Tao Lu, Cheng |
| contents | Realistic video simulation has shown significant potential across diverse applications, from virtual reality to film production. This is particularly true for scenarios where capturing videos in real-world settings is either impractical or expensive. Existing approaches in video simulation often fail to accurately model the lighting environment, represent the object geometry, or achieve high levels of photorealism. In this paper, we propose Anything in Any Scene, a novel and generic framework for realistic video simulation that seamlessly inserts any object into an existing dynamic video with a strong emphasis on physical realism. Our proposed general framework encompasses three key processes: 1) integrating a realistic object into a given scene video with proper placement to ensure geometric realism; 2) estimating the sky and environmental lighting distribution and simulating realistic shadows to enhance the light realism; 3) employing a style transfer network that refines the final video output to maximize photorealism. We experimentally demonstrate that Anything in Any Scene framework produces simulated videos of great geometric realism, lighting realism, and photorealism. By significantly mitigating the challenges associated with video data generation, our framework offers an efficient and cost-effective solution for acquiring high-quality videos. Furthermore, its applications extend well beyond video data augmentation, showing promising potential in virtual reality, video editing, and various other video-centric applications. Please check our project website https://anythinginanyscene.github.io for access to our project code and more high-resolution video results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17509 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Anything in Any Scene: Photorealistic Video Object Insertion Bai, Chen Shao, Zeman Zhang, Guoxiang Liang, Di Yang, Jie Zhang, Zhuorui Guo, Yujian Zhong, Chengzhang Qiu, Yiqiao Wang, Zhendong Guan, Yichen Zheng, Xiaoyin Wang, Tao Lu, Cheng Computer Vision and Pattern Recognition Realistic video simulation has shown significant potential across diverse applications, from virtual reality to film production. This is particularly true for scenarios where capturing videos in real-world settings is either impractical or expensive. Existing approaches in video simulation often fail to accurately model the lighting environment, represent the object geometry, or achieve high levels of photorealism. In this paper, we propose Anything in Any Scene, a novel and generic framework for realistic video simulation that seamlessly inserts any object into an existing dynamic video with a strong emphasis on physical realism. Our proposed general framework encompasses three key processes: 1) integrating a realistic object into a given scene video with proper placement to ensure geometric realism; 2) estimating the sky and environmental lighting distribution and simulating realistic shadows to enhance the light realism; 3) employing a style transfer network that refines the final video output to maximize photorealism. We experimentally demonstrate that Anything in Any Scene framework produces simulated videos of great geometric realism, lighting realism, and photorealism. By significantly mitigating the challenges associated with video data generation, our framework offers an efficient and cost-effective solution for acquiring high-quality videos. Furthermore, its applications extend well beyond video data augmentation, showing promising potential in virtual reality, video editing, and various other video-centric applications. Please check our project website https://anythinginanyscene.github.io for access to our project code and more high-resolution video results. |
| title | Anything in Any Scene: Photorealistic Video Object Insertion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.17509 |