Anything in Any Scene: Photorealistic Video Object Insertion
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 45 of its 60 references that are in the map. First posted on arXiv 30 Jan 2024.
Builds on
45 of 60 references.
References · 45
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33 more
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Built on by
Citing papers per year: 2024: 1; 2025: 4; 2026: 4.
9 papers.
- 2024Event-guided Low-light Video Semantic Segmentationcited by 14
- 2026PISCO: Precise Video Instance Insertion with Sparse Controlcited by 10
- 2025DreamInsert: Zero-Shot Image-to-Video Object Insertion from A Single Imagecited by 6
- 2026Versatile Editing of Video Content, Actions, and Dynamics without Trainingcited by 4
- 2026Smart-Insertion-V: Photorealistic Video Insertion via a Closed-Loop Feedback Dual-Stream Frameworkcited by 3
- 2025SynthDrive: Scalable Real2Sim2Real Sensor Simulation Pipeline for High-Fidelity Asset Generation and Driving Data Synthesiscited by 1
- 2025Local Background Features Matter in Out-of-Distribution Detectioncited by 0
- 2025InsertAnywhere: Geometrically Grounded and Optics-Aware Video Object Insertioncited by 0
- 2026Place-it-R1: Unlocking Environment-aware Reasoning Potential of MLLM for Video Object Insertioncited by 0
- 2026Smart-Insertion-V: Photorealistic Video Insertion via a Closed-Loop Feedback Dual-Stream Framework22 May 2026
- 2026Versatile Editing of Video Content, Actions, and Dynamics without Training18 Mar 2026
- 2026Place-it-R1: Unlocking Environment-aware Reasoning Potential of MLLM for Video Object Insertion6 Mar 2026
- 2026PISCO: Precise Video Instance Insertion with Sparse Control9 Feb 2026
- 2025InsertAnywhere: Geometrically Grounded and Optics-Aware Video Object Insertion19 Dec 2025
- 2025Local Background Features Matter in Out-of-Distribution Detection14 Oct 2025
- 2025SynthDrive: Scalable Real2Sim2Real Sensor Simulation Pipeline for High-Fidelity Asset Generation and Driving Data Synthesis8 Sep 2025
- 2025DreamInsert: Zero-Shot Image-to-Video Object Insertion from A Single Image13 Mar 2025
- 2024Event-guided Low-light Video Semantic Segmentation1 Nov 2024