GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting
(2311.14521)Abstract
3D editing plays a crucial role in many areas such as gaming and virtual reality. Traditional 3D editing methods, which rely on representations like meshes and point clouds, often fall short in realistically depicting complex scenes. On the other hand, methods based on implicit 3D representations, like Neural Radiance Field (NeRF), render complex scenes effectively but suffer from slow processing speeds and limited control over specific scene areas. In response to these challenges, our paper presents GaussianEditor, an innovative and efficient 3D editing algorithm based on Gaussian Splatting (GS), a novel 3D representation. GaussianEditor enhances precision and control in editing through our proposed Gaussian semantic tracing, which traces the editing target throughout the training process. Additionally, we propose Hierarchical Gaussian splatting (HGS) to achieve stabilized and fine results under stochastic generative guidance from 2D diffusion models. We also develop editing strategies for efficient object removal and integration, a challenging task for existing methods. Our comprehensive experiments demonstrate GaussianEditor's superior control, efficacy, and rapid performance, marking a significant advancement in 3D editing. Project Page: https://buaacyw.github.io/gaussian-editor/
Overview
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GaussianEditor is a new algorithm for quick and controllable 3D editing using Gaussian Splatting.
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It introduces 'Gaussian semantic tracing' for precise edits by only modifying semantically tagged areas within the 3D model.
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The 'Hierarchical Gaussian Splatting' technique adapts the Gaussian Splatting structure for a range of editing scenarios with detail optimization.
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A specialized 3D inpainting process allows for efficient object removal and integration within scenes.
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Testing shows GaussianEditor's superior control and effectiveness, with the 3D editing process taking 5 to 10 minutes.
Background on 3D Editing
3D editing is a key component in numerous fields such as gaming and virtual reality. Traditionally, this task has been accomplished using methods that provide interactivity and control over objects, such as meshes and point clouds. However, these approaches often struggle to realistically depict complex scenes. On the other hand, implicit 3D representations like Neural Radiance Fields (NeRF) essentially changed the game by rendering intricate scenes effectively but are known for being computationally intensive, particularly in terms of editing and processing speed.
Introducing GaussianEditor
To overcome the limitations of existing 3D editing methods, a new algorithm named GaussianEditor has been developed. This innovation is designed to perform 3D editing tasks swiftly and with a high degree of control. It builds upon a concept known as Gaussian Splatting (GS), an explicit 3D representation allowing for real-time rendering and editable point cloud-like structures. GaussianEditor makes the process of editing 3D scenes more flexible and rapid by integrating text-based editing with explicit control methods such as the use of bounding boxes for specific area modifications.
Precision and Control in Editing
A cornerstone of the GaussianEditor algorithm is the introduction of "Gaussian semantic tracing." This approach improves editing precision by tracing the changes throughout the training process, which is in stark contrast to the static masks used in traditional methods. With Gaussian semantic tracing, each Gaussian point in the 3D model is tagged semantically, allowing the system to only modify targeted areas with high accuracy.
Further refining the method, "Hierarchical Gaussian Splatting" (HGS) is proposed to ensure that the GS adapts to a wider range of editing scenarios. In HGS, Gaussians are organized into generations and application of stricter constraints on older generations, thus optimizing the balance between detail orientation and adaptability.
Streamlining 3D Inpainting
An additional contribution from the research is a specialized 3D inpainting process for GS, which includes both the removal and integration of objects in a scene. For instance, the elimination of an object is aided by a local repair algorithm that removes artifacts at the interface between the object and the scene quickly and efficiently. For object addition, users provide a text prompt and a 2D inpainting mask from a particular view. An image of the object to be added is generated and then transformed into 3D Gaussians which are finally integrated into the original scene.
Performance and Efficacy
Experiments with GaussianEditor demonstrate its superior control, effectiveness, and speed in editing, marking a significant advance in the area of 3D scene manipulation. Additionally, the entire editing process is drastically faster, usually taking between 5 to 10 minutes, which is a notable improvement over previous methods.
Conclusion
The development of GaussianEditor is a step forward for efficient and controlled 3D scene editing using Gaussian Splatting. By incorporating semantic tracing and HGS, alongside the specialized 3D inpainting process, GaussianEditor fulfills high-quality editing needs within minutes, significantly enhancing the controllability and practicality of 3D editing.
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