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Anything in Any Scene: Photorealistic Video Object Insertion

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arxiv 2401.17509 v1 pith:NPX4IMLJ submitted 2024-01-30 cs.CV

classification cs.CV
keywords videorealismframeworkobjectrealisticsceneanythingapplications
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. InsertAnywhere: Geometrically Grounded and Optics-Aware Video Object Insertion

    cs.CV 2025-12 conditional novelty 6.0 of 10

    InsertAnywhere inserts a reference object into arbitrary videos by reconstructing 4D geometry to propagate a user-given placement across frames and fine-tuning video diffusion on ROSE++, a removal-to-insertion dataset...

  2. SynthDrive: Scalable Real2Sim2Real Sensor Simulation Pipeline for High-Fidelity Asset Generation and Driving Data Synthesis

    cs.CV 2025-09 conditional novelty 5.0 of 10

    SynthDrive automatically mines images of rare objects, reconstructs them as 3D assets from a single view, and synthesizes driving footage that modestly improves detection of those objects.

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