CAGE-GS: High-fidelity Cage Based 3D Gaussian Splatting Deformation
This addresses the need for user-friendly deformation in 3D scene representation for applications like computer graphics and virtual reality, though it is incremental as it builds on existing 3DGS techniques.
The paper tackles the problem of deforming 3D Gaussian Splatting scenes to match user-defined shapes while preserving fine details, achieving significant improvements in efficiency and deformation quality over existing methods.
As 3D Gaussian Splatting (3DGS) gains popularity as a 3D representation of real scenes, enabling user-friendly deformation to create novel scenes while preserving fine details from the original 3DGS has attracted significant research attention. We introduce CAGE-GS, a cage-based 3DGS deformation method that seamlessly aligns a source 3DGS scene with a user-defined target shape. Our approach learns a deformation cage from the target, which guides the geometric transformation of the source scene. While the cages effectively control structural alignment, preserving the textural appearance of 3DGS remains challenging due to the complexity of covariance parameters. To address this, we employ a Jacobian matrix-based strategy to update the covariance parameters of each Gaussian, ensuring texture fidelity post-deformation. Our method is highly flexible, accommodating various target shape representations, including texts, images, point clouds, meshes and 3DGS models. Extensive experiments and ablation studies on both public datasets and newly proposed scenes demonstrate that our method significantly outperforms existing techniques in both efficiency and deformation quality.