title: Anything in Any Scene: Photorealistic Video Object Insertion
summary: 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.
paper_authors._id: 65bb1ab0bd9e29f771f6f22b
paper_authors.name: Chen Bai
paper_authors._id: 65bb1ab0bd9e29f771f6f22c
paper_authors.name: Zeman Shao
paper_authors._id: 65bb1ab0bd9e29f771f6f22d
paper_authors.name: Guoxiang Zhang
paper_authors._id: 65bb1ab0bd9e29f771f6f22e
paper_authors.name: Di Liang
paper_authors._id: 65bb1ab0bd9e29f771f6f22f
paper_authors.name: Jie Yang
paper_authors._id: 65bb1ab0bd9e29f771f6f230
paper_authors.name: Zhuorui Zhang
paper_authors._id: 65bb1ab0bd9e29f771f6f231
paper_authors.name: Yujian Guo
paper_authors._id: 65bb1ab0bd9e29f771f6f232
paper_authors.name: Chengzhang Zhong
paper_authors._id: 65bb1ab0bd9e29f771f6f233
paper_authors.name: Yiqiao Qiu
paper_authors._id: 65bb1ab0bd9e29f771f6f234
paper_authors.name: Zhendong Wang
paper_authors._id: 65bb1ab0bd9e29f771f6f235
paper_authors.name: Yichen Guan
paper_authors._id: 65bb1ab0bd9e29f771f6f236
paper_authors.name: Xiaoyin Zheng
paper_authors._id: 65bb1ab0bd9e29f771f6f237
paper_authors.name: Tao Wang
paper_authors._id: 65bb1ab0bd9e29f771f6f238
paper_authors.name: Cheng Lu
paper_title: Anything in Any Scene: Photorealistic Video Object Insertion
paper_summary: 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.
paper_ai_keywords: realistic object integration geometric realism sky estimation environmental lighting distribution realistic shadows style transfer network photorealism video data generation virtual reality video editing