Title: CAGE-GS: High-fidelity Cage Based 3D Gaussian Splatting Deformation Show Chinese title

Title: CAGE-GS：基于高保真笼子的三维高斯点阵变形

Abstract: 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. Show Chinese abstract

Abstract: 随着3D高斯点阵(3DGS)作为真实场景的三维表示越来越受欢迎，它使用户友好的变形创建新场景的同时保留原始3DGS的细节吸引了大量的研究关注。 我们介绍了CAGE-GS，这是一种基于笼子的3DGS变形方法，可以无缝地将源3DGS场景与用户定义的目标形状对齐。 我们的方法从目标形状学习一个变形笼子，该笼子指导源场景的几何变换。 虽然这些笼子有效地控制了结构对齐，但由于协方差参数的复杂性，保留3DGS的纹理外观仍然具有挑战性。 为了解决这个问题，我们采用了一种基于雅可比矩阵的策略来更新每个高斯点的协方差参数，确保变形后的纹理保真度。 我们的方法非常灵活，能够适应各种目标形状表示，包括文本、图像、点云、网格和3DGS模型。 广泛的实验和消融研究在公共数据集和新提出的场景上表明，我们的方法在效率和变形质量方面显著优于现有技术。