Abstract
Capturing and re-animating the 3D structure of articulated objects present significant barriers. On one hand, methods requiring extensively calibrated multi-view setups are prohibitively complex and resource-intensive, limiting their practical applicability. On the other hand, while single-camera Neural Radiance Fields (NeRFs) offer a more streamlined approach, they have excessive training and rendering costs. 3D Gaussian Splatting would be a suitable alternative but for two reasons. Firstly, existing methods for 3D dynamic Gaussians require synchronized multi-view cameras, and secondly, the lack of controllability in dynamic scenarios. We present CoGS, a method for Controllable Gaussian Splatting, that enables the direct manipulation of scene elements, offering real-time control of dynamic scenes without the prerequisite of pre-computing control signals. We evaluated CoGS using both synthetic and real-world datasets that include dynamic objects that differ in degree of difficulty. In our evaluations, CoGS consistently outperformed existing dynamic and controllable neural representations in terms of visual fidelity.
Overview
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Introduces Controllable Gaussian Splatting (CoGS) for efficient and controllable modeling of dynamic scenes.
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Describes a novel framework that separates dynamic scene modeling from attribute control, allowing for real-time scene manipulation.
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Evaluates CoGS against existing methods, demonstrating its superiority in rendering quality and manipulation capability.
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Highlights the potential of CoGS to revolutionize fields like virtual reality, augmented reality, and interactive media.
Controllable Gaussian Splatting for Dynamic Scene Manipulation
Introduction
The field of computer vision and 3D reconstruction has progressed significantly with the advent of Neural Radiance Fields (NeRF) and their derivatives, facilitating high-fidelity novel-view synthesis and scene representation. Despite their success, NeRF-based approaches, particularly when applied to dynamic scenes, encounter challenges including prohibitive computational costs and complexities in scene manipulation. Recognizing these limitations, the paper introduces Controllable Gaussian Splatting (CoGS) as an enhanced method that not only models dynamic scenes efficiently but also provides an intuitive mechanism for manipulating scene elements in real-time without pre-computing control signals.
Related Works
The paper delineates the landscape of dynamic scene modeling, highlighting the evolution from traditional NeRFs to dynamic and controllable NeRF extensions. It reviews the challenges inherent in extending NeRF to dynamic scenes such as the need for extensive calibration in multi-view setups and the complexity of manipulating implicit scene representations. Concurrently, it explores initiatives in Gaussian Splatting, identifying its potential for explicit scene modeling and real-time manipulation if extended to encompass dynamic scenarios.
Methods
CoGS proposes a novel framework that bifurcates the process into dynamic scene modeling followed by attribute control. The method begins by establishing a dynamic Gaussian Splatting model capable of handling scene deformations effectively. This is achieved through defining 3D Gaussians with attributes like position, variance, and color, and projecting these into 2D space for rendering. The method stands out by learning deformation fields for each Gaussian parameter independently, facilitated by differentiable rasterization, and employing multi-faceted loss functions to ensure geometric consistency over time.
For the controllable aspect, CoGS introduces a mechanism to project 2D masks into 3D space, enabling precise control over specific scene elements. This is extended by an unsupervised learning approach for control signal extraction directly from Gaussian representations, paving the path for manipulating these signals to achieve desired scene configurations.
Dynamic Gaussian Splatting
This section explore the mechanics of representing scene dynamics using 3D Gaussians. It outlines the process for defining, optimizing, and rendering these Gaussians to encapsulate scene deformations. The approach is elaborated through a series of innovative steps including the integration of deformation networks, imposition of multiple regularization losses, and a comprehensive optimization strategy to refine scene representation.
Controllable Framework Extension
Building upon the dynamism introduced earlier, this phase introduces the procedural details for extending GS into a controllable framework. It covers 3D mask generation, control signal extraction, and signal re-alignment, explaining how these components synergize to facilitate direct manipulation of scene elements. The method's ingenuity is showcased in its ability to derive and adjust control signals for dynamic scene manipulation, demonstrably advancing beyond the capacities of existing NeRF-based models.
Experiments
The paper meticulously evaluates CoGS against benchmark techniques across synthetic and real-world datasets, demonstrating its superiority in visual fidelity and controllable manipulation. Quantitative analyses underscore the method's effectiveness, showcasing significant improvements in metrics like PSNR, SSIM, and LPIPS across various dynamic scenes. Qualitative evaluations further affirm the method's capacity to accurately model complex scene dynamics and execute fine-grained manipulations with minimal artifacts.
Conclusion
CoGS represents a significant leap forward in dynamic scene modeling and manipulation. By leveraging the explicit representation of Gaussian Splatting, this method not only ensures efficient scene rendering but also simplifies the process of scene element manipulation. The implications of this research are vast, potentially transforming fields such as virtual reality, augmented reality, and interactive media with the capability for real-time, high-fidelity scene rendering and manipulation. The paper posits that future advancements may further refine and extend this method, broadening the horizons for dynamic and interactive 3D content creation.