GSDeformer: Direct, Real-time and Extensible Cage-based Deformation for 3D Gaussian Splatting (2405.15491v3)

Abstract: We present GSDeformer, a method that enables cage-based deformation on 3D Gaussian Splatting (3DGS). Our approach bridges cage-based deformation and 3DGS by using a proxy point-cloud representation. This point cloud is generated from 3D Gaussians, and deformations applied to the point cloud are translated into transformations on the 3D Gaussians. To handle potential bending caused by deformation, we incorporate a splitting process to approximate it. Our method does not modify or extend the core architecture of 3D Gaussian Splatting, making it compatible with any trained vanilla 3DGS or its variants. Additionally, we automate cage construction for 3DGS and its variants using a render-and-reconstruct approach. Experiments demonstrate that GSDeformer delivers superior deformation results compared to existing methods, is robust under extreme deformations, requires no retraining for editing, runs in real-time, and can be extended to other 3DGS variants. Project Page: https://jhuangbu.github.io/gsdeformer/

• The paper introduces a direct cage-based deformation method that applies free-form changes to 3D Gaussian Splatting without modifying its base architecture.
• It converts 3DGS into a proxy point cloud and uses an automated cage-building algorithm to streamline noise removal and mesh extraction.
• Experimental results demonstrate that GSDeformer achieves deformation quality comparable to complex methods while enabling efficient, real-time editing in animation and VR applications.

GSDeformer: Direct Cage-based Deformation for 3D Gaussian Splatting

The paper introduces GSDeformer, a method enabling free-form deformation on 3D Gaussian Splatting (3DGS) without requiring architectural modifications. This work extends the traditional cage-based deformation technique, typically used for mesh deformation, to the 3DGS context. By converting 3DGS into a proxy point cloud representation, deformations can be inferred and subsequently applied to the underlying Gaussian distributions. An automated cage construction algorithm for 3DGS enhances the usability of the approach, minimizing the need for manual interventions.

Introduction and Motivation

3D Gaussian Splatting has demonstrated substantial success in capturing and representing complex real-world scenes. Nevertheless, to render such representations useful for practical applications like animation, virtual reality, or augmented reality, manipulation capabilities are essential. Existing solutions requiring complex architectural changes or additional data sources make editing pre-trained 3DGS models cumbersome. The proposed GSDeformer addresses these limitations by offering a direct and intuitive method for scene manipulation without altering the foundational architecture of 3DGS.

Methodology

The methodology is divided into two primary components: cage-building and deformation.

Cage-Building Algorithm

The cage-building process starts by converting the 3DGS representation into a binary occupancy voxel grid. This grid is processed for noise removal through morphological closing, followed by mesh contour extraction via the marching cubes algorithm. Smoothing of the generated mesh and subsequent decimation using edge-collapse techniques result in a coarse cage that encapsulates the 3DGS scene.

Deformation Algorithm

1. Distribution to Ellipsoid: Each 3D Gaussian distribution is transformed into an isocontour ellipsoid based on its mean and covariance matrix. The principal axes and lengths of these ellipsoids are computed.
2. Ellipsoid to Axis Points: These ellipsoids are represented using four primary axis points, simplifying the deformation process.
3. Deform Points with Cage-Based Deformation: The method utilizes cage-based deformation to manipulate these axis points based on the source and target cages.
4. Infer Affine Transform: An affine transformation matrix is inferred by comparing the original and deformed positions of the axis points. This matrix encapsulates translation, rotation, and scaling effects.
5. Apply Transform: The inferred affine transform is applied to the means and covariance matrices of the Gaussian distributions, thereby performing the desired deformation.

Experimental Results

The experiments demonstrate the effectiveness of GSDeformer on both synthetic and real-world scenes. The method's ability to handle various forms of deformations, such as twisting, lifting, and expanding objects, is validated visually and through quantitative comparisons. The results indicate that GSDeformer offers deformation quality comparable to more complex methods while requiring fewer changes to the underlying 3DGS architecture.

Implications and Future Work

The implications of this work are twofold. Practically, GSDeformer simplifies scene manipulation tasks, potentially accelerating workflows in animation and virtual/augmented reality production pipelines. Theoretically, the methodology presents a novel integration of cage-based deformation with point-cloud representations, potentially inspiring future research in other forms of scene representation and manipulation.

Future directions could explore optimizing the deformation process further, integrating the method with other concurrent developments in 3DGS, and expanding the automatic cage construction algorithm to handle more complex scenes. Extending the method to support real-time deformations and interactive scene editing are also promising avenues for subsequent research.

In summary, GSDeformer presents a direct and efficient method for free-form deformation of 3D Gaussian Splatting scenes, maintaining high-quality results while simplifying integration with existing 3DGS models. This work paves the way for more accessible and flexible scene manipulation techniques in various digital applications.