Anything in Any Scene: Photorealistic Video Object Insertion
(2401.17509)Abstract
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.
Overview
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The paper discusses advancements in inserting 3D objects into dynamic video settings with high levels of realism.
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It presents a framework called 'Anything in Any Scene' which deals with geometric alignment, lighting, and visual authenticity challenges.
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The framework includes environment lighting estimation for realistic shadows, and a style transfer network to correct visual artifacts.
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Empirical results show the framework's superior performance in video realism, as evidenced by low FID scores and high human evaluation scores.
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Applications of the framework are highlighted in dataset augmentation for improving the performance of perception algorithms.
Introduction
The realm of video simulation for applications such as virtual reality and film production is advancing rapidly, particularly with the integration of objects into dynamic video environments. This integration must meet stringent standards of physical realism, which hinges on accurate geometric alignment, lighting harmony, and seamless photorealistic blending of inserted objects with existing video footage.
Framework Overview
The paper introduces "Anything in Any Scene," a comprehensive framework that champions the seamless combination of 3D objects in dynamic video settings, addressing the geometric, lighting, and visual authenticity that prior methodologies have struggled to achieve. The authors identify the necessity of considering the intricate complexities that come with outdoor environments and the complications in incorporating a variety of object classes.
A cornerstone of the framework is its ability to estimate environment lighting, including sky and environmental conditions, to yield realistic shadowing effects. The framework further extends its ingenuity through a style transfer network that refines visual artifacts, such as noise discrepancies or color imbalances, enhancing the integration of the inserted object into the video with heightened photorealism.
Numerical Results and Framework Applications
Empirical results validate the framework's superiority in achieving high degrees of geometric, lighting, and photorealistic realism. An impressive quantitative leap is indicated with the lowest FID score at 3.730 and the highest human score at 61.11%, affirming superior performance in video simulation realism. Further substantiation comes from its applications in perception algorithms, demonstrating its potential in augmenting datasets to improve the performance of object detection models.
The versatility of this framework facilitates the creation of large-scale, realistic video datasets for diverse domains, exemplifying an efficient and cost-effective method for video data augmentation. It addresses challenges such as long-tail distribution and successfully navigates the constraints of out-of-distribution exemplars.
Conclusion
The paper concludes by underscoring the pivotal role of the proposed framework in the innovation of video simulation technology. It's presented as a malleable substrate, open to future enhancements with improved models, and boons for emerging applications in various video-dependent fields. This work stands as a testament to the ongoing evolution in the fabrication of synthetic video content, where realism and practicality are paramount.
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