Anything in Any Scene: Photorealistic Video Object Insertion
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.
65bb1ab0bd9e29f771f6f22b
Chen Bai
65bb1ab0bd9e29f771f6f22c
Zeman Shao
65bb1ab0bd9e29f771f6f22d
Guoxiang Zhang
65bb1ab0bd9e29f771f6f22e
Di Liang
65bb1ab0bd9e29f771f6f22f
Jie Yang
65bb1ab0bd9e29f771f6f230
Zhuorui Zhang
65bb1ab0bd9e29f771f6f231
Yujian Guo
65bb1ab0bd9e29f771f6f232
Chengzhang Zhong
65bb1ab0bd9e29f771f6f233
Yiqiao Qiu
65bb1ab0bd9e29f771f6f234
Zhendong Wang
65bb1ab0bd9e29f771f6f235
Yichen Guan
65bb1ab0bd9e29f771f6f236
Xiaoyin Zheng
65bb1ab0bd9e29f771f6f237
Tao Wang
65bb1ab0bd9e29f771f6f238
Cheng Lu
Anything in Any Scene: Photorealistic Video Object Insertion
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.
A novel framework for realistic video simulation, AnyObject in Any Scene, achieves high geometric, lighting, and photorealism by integrating objects, simulating lighting and shadows, and refining video output through style transfer.
video simulation virtual reality object geometry photorealism style transfer network sky and environmental lighting shadows geometric realism lighting realism