We present GSDeformer, a method that achieves free-form deformation on 3D
Gaussian Splatting(3DGS) without requiring any architectural changes. Our
method extends cage-based deformation, a traditional mesh deformation method,
to 3DGS. This is done by converting 3DGS into a novel proxy point cloud
representation, where its deformation can be used to infer the transformations
to apply on the 3D gaussians making up 3DGS. We also propose an automatic cage
construction algorithm for 3DGS to minimize manual work. Our method does not
modify the underlying architecture of 3DGS. Therefore, any existing trained
vanilla 3DGS can be easily edited by our method. We compare the deformation
capability of our method against other existing methods, demonstrating the ease
of use and comparable quality of our method, despite being more direct and thus
easier to integrate with other concurrent developments on 3DGS.

我们提出了 GSDeformer，这是一种在不需要任何架构变动的情况下，在 3D Gaussian Splatting (3DGS) 上实现自由形变的方法。我们的方法将传统的网格变形方法 (基于 cage 的变形) 扩展到了 3DGS 上，通过将 3DGS 转换为新型的代理点云表示形式，可以推断出应用于构成 3DGS 的 3D 高斯函数的变换。同时，我们还提出了一种自动构建 3DGS cage 的算法，以减少手动工作。我们的方法不修改 3DGS 的基本架构，因此任何现有的已经训练好的标准 3DGS 都可以轻松地通过我们的方法进行编辑。通过与其他现有方法比较我们的方法的变形能力，我们证明了我们的方法的易于使用和可比较的质量，尽管更加直接，因此更容易与 3DGS 上的其他并行开发集成。

GSDeformer：基于直接笼式变形的三维高斯点云投影

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

Recent advances in radiance fields enable photorealistic rendering of static
or dynamic 3D scenes, but still do not support explicit deformation that is
used for scene manipulation or animation. In this paper, we propose a method
that enables a new type of deformation of the radiance field: free-form
radiance field deformation. We use a triangular mesh that encloses the
foreground object called cage as an interface, and by manipulating the cage
vertices, our approach enables the free-form deformation of the radiance field.
The core of our approach is cage-based deformation which is commonly used in
mesh deformation. We propose a novel formulation to extend it to the radiance
field, which maps the position and the view direction of the sampling points
from the deformed space to the canonical space, thus enabling the rendering of
the deformed scene. The deformation results of the synthetic datasets and the
real-world datasets demonstrate the effectiveness of our approach.

本文提出了一种基于三角网格和笼状变形的方法，实现了光度场的自由形变，从而实现了可操纵和渲染的 3D 场景。

用笼子变形辐射场

Deforming Radiance Fields with Cages

We propose a novel learnable representation for detail-preserving shape
deformation. The goal of our method is to warp a source shape to match the
general structure of a target shape, while preserving the surface details of
the source. Our method extends a traditional cage-based deformation technique,
where the source shape is enclosed by a coarse control mesh termed \emph{cage},
and translations prescribed on the cage vertices are interpolated to any point
on the source mesh via special weight functions. The use of this sparse cage
scaffolding enables preserving surface details regardless of the shape's
intricacy and topology. Our key contribution is a novel neural network
architecture for predicting deformations by controlling the cage. We
incorporate a differentiable cage-based deformation module in our architecture,
and train our network end-to-end. Our method can be trained with common
collections of 3D models in an unsupervised fashion, without any cage-specific
annotations. We demonstrate the utility of our method for synthesizing shape
variations and deformation transfer.

本文提出了一种可学习的表示方法，用于形变中保留细节，其核心是一种基于 cage 的形变技术并且结合了神经网络来控制 cage 的变形以保留细节，可在无监督学习的情况下进行训练，并且在形状变化和变形传输方面具有实用价值。