ARAP-GS：基于扩散先验的拖动驱动型尽可能刚性（As-Rigid-As-Possible）3D高斯溅射编辑
ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior
AI总结：
针对3D高斯溅射（3DGS）拖动驱动编辑中形状连贯性与视觉连续性难保持的问题，提出ARAP-GS框架，首次将ARAP变形直接应用于3D高斯，结合扩散先验提升视觉质量与多视图一致性，实验证明其优于现有方法且效率高。
摘要：
拖动驱动编辑因能通过简单直观的操作修改复杂几何结构，让用户以最少技术技能调整和重塑内容，在设计师中广受欢迎。这种拖动操作已被整合到多种方法中，以促进设计中2D图像和3D网格的编辑。然而，针对广泛使用的3D高斯溅射（3DGS）表示的拖动驱动编辑研究较少，因为在变形3DGS时保持形状连贯性和视觉连续性仍具挑战性。本文提出ARAP-GS，一种基于尽可能刚性（ARAP）变形的拖动驱动3DGS编辑框架。与以往3DGS编辑方法不同，我们首次将ARAP变形直接应用于3D高斯，实现灵活的拖动驱动几何变换。为保持变形后的场景外观，我们在迭代优化过程中融入了用于图像超分辨率的先进扩散先验，该方法在保持编辑结果多视图一致性的同时提升了视觉质量。实验表明，ARAP-GS在多种3D场景中优于现有方法，证明其在拖动驱动3DGS编辑中的有效性和优越性；此外，该方法效率高，在单个RTX 3090 GPU上编辑一个场景仅需10至20分钟。
英文摘要：
Drag-driven editing has become popular among designers for its ability to modify complex geometric structures through simple and intuitive manipulation, allowing users to adjust and reshape content with minimal technical skill. This drag operation has been incorporated into numerous methods to facilitate the editing of 2D images and 3D meshes in design. However, few studies have explored drag-driven editing for the widely-used 3D Gaussian Splatting (3DGS) representation, as deforming 3DGS while preserving shape coherence and visual continuity remains challenging. In this paper, we introduce ARAP-GS, a drag-driven 3DGS editing framework based on As-Rigid-As-Possible (ARAP) deformation. Unlike previous 3DGS editing methods, we are the first to apply ARAP deformation directly to 3D Gaussians, enabling flexible, drag-driven geometric transformations. To preserve scene appearance after deformation, we incorporate an advanced diffusion prior for image super-resolution within our iterative optimization process. This approach enhances visual quality while maintaining multi-view consistency in the edited results. Experiments show that ARAP-GS outperforms current methods across diverse 3D scenes, demonstrating its effectiveness and superiority for drag-driven 3DGS editing. Additionally, our method is highly efficient, requiring only 10 to 20 minutes to edit a scene on a single RTX 3090 GPU.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
