GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis
(2312.11458)Abstract
We propose a method for dynamic scene reconstruction using deformable 3D Gaussians that is tailored for monocular video. Building upon the efficiency of Gaussian splatting, our approach extends the representation to accommodate dynamic elements via a deformable set of Gaussians residing in a canonical space, and a time-dependent deformation field defined by a multi-layer perceptron (MLP). Moreover, under the assumption that most natural scenes have large regions that remain static, we allow the MLP to focus its representational power by additionally including a static Gaussian point cloud. The concatenated dynamic and static point clouds form the input for the Gaussian Splatting rasterizer, enabling real-time rendering. The differentiable pipeline is optimized end-to-end with a self-supervised rendering loss. Our method achieves results that are comparable to state-of-the-art dynamic neural radiance field methods while allowing much faster optimization and rendering. Project website: https://lynl7130.github.io/gaufre/index.html
Overview
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GauFRe introduces a method for real-time dynamic scene reconstruction using a combination of dynamic and static 3D Gaussians.
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The methodology employs a multi-layer perceptron (MLP) to define a time-dependent deformation field for dynamic regions, optimizing with a self-supervised rendering loss.
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GauFRe achieves quality comparable to state-of-the-art solutions, but with faster optimization and real-time rendering capabilities.
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Incorporating static Gaussians for unchanged areas allows the MLP to focus on dynamic elements, enhancing efficiency and final image quality.
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The technique offers potential for interactive applications in virtual reality, gaming, and video editing.
Introduction
The field of 3D reconstruction from 2D images poses numerous challenges, particularly when reconstructing dynamic scenes from a single, moving camera's footage. Previous methods for addressing this issue rely on a diverse range of techniques, many of which come with their own sets of trade-offs concerning quality, optimization speed, and rendering speed. A new method, GauFRe, leverages the efficiency of Gaussian splatting extended with deformable 3D Gaussians, to accommodate dynamic changes, offering a favorable balance of high-quality reconstruction with real-time rendering capabilities.
Dynamic Scene Reconstruction Methodology
GauFRe differentiates itself by utilizing a multi-layer perceptron (MLP) to define a time-dependent deformation field, transforming a canonical arrangement of Gaussians to represent movement and deformation within a scene. Furthermore, the method acknowledges that natural scenes often contain large static regions. By employing both dynamic and static Gaussians—where static regions are represented by a separate, undeformable set—the MLP can concentrate on representing dynamic elements more accurately. The process involves optimizing the dynamic and static Gaussians with a self-supervised rendering loss, allowing for more efficient computing resource allocation and improved final image quality.
Performance and Validation
When evaluating GauFRe against several baselines on both synthetic and real-world datasets, it achieves quality on par with state-of-the-art alternatives while ensuring faster optimization and real-time rendering. These attributes suggest notable advantages in scenarios requiring rapid deployment or interactive applications. The deformation of Gaussians, coupled with the incorporation of a static Gaussian point cloud, delivers higher quality reconstructions with less computational expense.
Conclusion on GauFRe's Implications
The innovation brought forward by GauFRe marks a significant contribution. Not only does it provide a tool for rapid and high-quality reconstruction of dynamic scenes from monocular video inputs, but it also opens doors for applications in virtual reality, gaming, and video editing where real-world dynamic events need to be recreated and manipulated with high fidelity and efficiency. The dynamic/static separation is a particularly clever feature that allows more focused processing on movement within a scene, highlighting the potential for even more nuanced adaptations and extensions of this method in the future.