3D Gaussian Splatting (GS) is one of the most promising novel 3D representations that has received great interest in computer graphics and computer vision. While various systems have introduced editing capabilities for 3D GS, such as those guided by text prompts, fine-grained control over deformation remains an open challenge. In this work, we present a novel sketch-guided 3D GS deformation system that allows users to intuitively modify the geometry of a 3D GS model by drawing a silhouette sketch from a single viewpoint. Our approach introduces a new deformation method that combines cage-based deformations with a variant of Neural Jacobian Fields, enabling precise, fine-grained control. Additionally, it leverages large-scale 2D diffusion priors and ControlNet to ensure the generated deformations are semantically plausible. Through a series of experiments, we demonstrate the effectiveness of our method and showcase its ability to animate static 3D GS models as one of its key applications.
3D Gaussian Splatting (GS) 是一种备受关注的新型3D表示方法，在计算机图形学和计算机视觉领域具有广阔的前景。尽管已有各种系统为3D GS引入了编辑功能，例如基于文本提示的引导，但对变形的精细控制仍然是一个未解决的挑战。在本研究中，我们提出了一种新颖的 草图引导3D GS变形系统，允许用户通过从单一视角绘制轮廓草图直观地修改3D GS模型的几何形状。 我们的方法引入了一种全新的变形方法，结合了基于笼形变形（cage-based deformation）与神经雅可比场（Neural Jacobian Fields）的变体，实现了精确的细粒度控制。此外，系统还利用大规模2D扩散先验和 ControlNet，确保生成的变形在语义上具有合理性。通过一系列实验，我们验证了该方法的有效性，并展示了其将静态3D GS模型动画化的关键应用之一。