Anything in Any Scene: Realistic object insertion (to assist in the synthesis of various driving data)

Original title: Anything in Any Scene: Photorealistic Video Object Insertion

Paper link: https://arxiv.org/pdf/2401.17509.pdf

Code link: https ://github.com/AnythingInAnyScene/anything_in_anyscene

Author affiliation: Xpeng Motors

##Thesis idea

Realistic video simulation shows huge potential in applications ranging from virtual reality to film production. Especially when capturing video in the real world is impractical or expensive. Existing methods in video simulation often fail to accurately model lighting environments, represent object geometry, or achieve high levels of photorealism. This paper proposes

Anything in Any Scene, a novel and versatile real video simulation framework that can seamlessly insert any object into existing dynamic videos and emphasize physical realism. The overall framework proposed in this paper contains three key processes: 1) integrating real objects into a given scene video and placing them in appropriate locations to ensure geometric realism; 2) estimating sky and ambient illumination distribution and Simulate real shadows and enhance light realism; 3) Use a style transfer network to refine the final video output to maximize photo realism. This article experimentally proves that the Anything in Any Scene framework can generate simulation videos with excellent geometric realism, lighting realism, and photo realism. By significantly mitigating the challenges associated with video data generation, our framework provides an efficient and cost-effective solution for obtaining high-quality videos. Furthermore, its applications extend far beyond video data enhancement, showing promising potential in virtual reality, video editing, and various other video-centric applications.

Main Contribution

This paper introduces a novel and extensible Anything in Any Scene video simulation framework, capable of integrating any object into any dynamic scene video.

This article is uniquely structured and focuses on maintaining geometry, lighting, and photorealism in video simulations to ensure high quality and authenticity of the output results.

After extensive verification, the results show that the framework has the ability to produce highly realistic video simulations, thus significantly expanding the application scope and development potential of this field.

Thesis Design

Image and video simulation have found success in a variety of applications, from virtual reality to film production. The ability to generate diverse and high-quality visual content through photorealistic image and video simulation has the potential to advance these fields, introducing new possibilities and applications. While the authenticity of images and videos captured in the real world is invaluable, they are often limited by long-tail distributions. This leads to over-representation of common scenarios and under-representation of rare but critical situations, presenting a challenge known as the out-of-distribution problem. Traditional methods of addressing these limitations through video capture and editing proved impractical or cost-prohibitive because it was difficult to cover all possible scenarios. The importance of video simulation, especially by integrating existing videos with newly inserted objects, becomes crucial to overcome these challenges. By generating large-scale, diverse, and realistic visual content, video simulation helps augment applications in virtual reality, video editing, and video data augmentation.

However, generating realistic simulation videos considering physical realism is still a challenging open problem. Existing methods often exhibit limitations by focusing on specific settings, especially indoor environments [9, 26, 45, 46, 57]. These methods may not adequately address the complexity of outdoor scenes, including varying lighting conditions and fast-moving objects. Methods relying on 3D model registration are limited to integrating limited classes of objects [12, 32, 40, 42]. Many methods ignore important factors such as lighting environment modeling, correct object placement, and achieving realism [12, 36]. The failed case is shown in Figure 1. Therefore, these limitations greatly limit their application in areas that require highly scalable, geometrically consistent, and realistic scene video simulations, such as autonomous driving and robotics.

This paper proposes a comprehensive framework for photorealistic video object insertion in Anything in Any Scene that addresses these challenges. The framework is designed to be versatile and suitable for indoor and outdoor scenes, ensuring physical accuracy in terms of geometric realism, lighting realism, and photorealism. The goal of this paper is to create video simulations that are not only useful for visual data augmentation in machine learning, but also suitable for various video applications such as virtual reality and video editing.

The overview of the Anything in Any Scene framework of this article is shown in Figure 2. This paper details our novel and scalable pipeline for building a diverse asset library of scene videos and object meshes in Section 3. This paper introduces a visual data query engine designed to efficiently retrieve relevant video clips from visual queries using descriptive keywords. Next, this paper proposes two methods for generating 3D meshes, leveraging existing 3D assets as well as multi-view image reconstruction. This allows unrestricted insertion of any desired object, even if it is highly irregular or semantically weak. In Section 4, the paper details methods for integrating objects into dynamic scene videos, focusing on maintaining physical realism. This paper designs the object placement and stabilization method described in Section 4.1 to ensure that the inserted object is stably anchored on consecutive video frames. To address the challenge of creating realistic lighting and shadow effects, this paper estimates sky and environment lighting and generates realistic shadows during rendering, as described in Section 4.2. The generated simulated video frames inevitably contain unrealistic artifacts that differ from real-world captured video, such as imaging quality differences in noise levels, color fidelity, and sharpness. This paper uses a style transfer network to enhance photo realism in Section 4.3.

The simulation videos generated from the framework proposed in this paper achieve a high degree of lighting realism, geometric realism, and photo realism, outperforming other videos in both quality and quantity, as shown in Section 5.3. This article further demonstrates the application of this article's simulation video in training perception algorithms in Section 5.4 to verify its practical value. The Anything in Any Scene framework enables the creation of large-scale, low-cost video datasets for data augmentation with time-efficiency and realistic visual quality, thereby easing the burden of video data generation and potentially improving long-tail and out-of-distribution challenges . With its general framework design, the Anything in Any Scene framework can easily integrate improved models and new modules, such as improved 3D mesh reconstruction methods, to further enhance video simulation performance.

Figure 1. Examples of simulated video frames with incorrect lighting environment estimation, incorrect object placement, and unrealistic texture styles. These problems make the image lack physical realism. Figure 2. Overview of the Anything in Any Scene framework for photorealistic video object insertionFigure 3. Example of a driving scene video for object placement. The red dots in each image are where the objects were inserted.

Experimental results

Figure 4. Examples of original sky images, reconstructed HDR images, and their associated solar illumination distribution maps

Figure 5. Examples of original and reconstructed HDR environment panoramic images

Figure 6. For inserted objects Example of shadow generation

# Figure 7. Qualitative comparison of simulated video frames from the PandaSet dataset using different style transfer networks.

Figure 8. Qualitative comparison of simulated video frames from the PandaSet dataset under various rendering conditions.

Summary:

This paper proposes an innovative and extensible framework, "Anything in Any Scene", designed for realistic video simulation And design. The framework proposed in this paper seamlessly integrates various objects into different dynamic videos, ensuring that geometric realism, lighting realism, and photo realism are preserved. Through extensive demonstrations, this paper demonstrates its efficacy in mitigating challenges associated with video data collection and generation, providing a cost-effective and time-saving solution for a variety of scenarios. The application of our framework shows significant improvements in downstream perception tasks, especially in solving the long-tail distribution problem in object detection. The flexibility of our framework allows direct integration of improved models for each module, and our framework lays a solid foundation for future exploration and innovation in the field of realistic video simulation.

Citation:

Bai C, Shao Z, Zhang G, et al. Anything in Any Scene: Photorealistic Video Object Insertion[J]. arXiv preprint arXiv:2401.17509 , 2024.

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