通过拖拽控制点实现3D高斯点云的精准几何编辑
Drag Your Gaussian: Effective Drag-Based Editing with Score Distillation for 3D Gaussian Splatting
- 用3D掩码和控制点对指定区域进行拖拽编辑
- 在多视角下保持一致性,显著优于现有方法
- 适合需要精确位置控制的3D场景编辑任务
近期3D场景编辑得益于生成模型的快速发展。现有方法通常利用生成模型对3D表示(如3D高斯点云)进行文本引导编辑,但大多仅限于纹理修改,难以处理几何变化(如角色头部转向)。此外,语言描述难以精确控制编辑范围。为此,本文提出DYG,一种针对3D高斯点云的高效拖拽式编辑方法。用户可通过3D掩码和控制点对指定编辑区域及方向进行精确设定,实现对编辑范围的精准控制。DYG结合隐式三平面表示构建编辑结果的几何骨架,有效克服了3DGS在目标区域稀疏导致的次优结果。同时,通过提出的拖拽式扩散损失(Drag-SDS),将拖拽式潜在扩散模型融入框架,实现灵活、多视角一致且细粒度的编辑。大量实验表明,DYG在定性和定量上均优于基线方法。
原文摘要 · Abstract (English)
Recent advancements in 3D scene editing have been propelled by the rapid development of generative models. Existing methods typically utilize generative models to perform text-guided editing on 3D representations, such as 3D Gaussian Splatting (3DGS). However, these methods are often limited to texture modifications and fail when addressing geometric changes, such as editing a character's head to turn around. Moreover, such methods lack accurate control over the spatial position of editing results, as language struggles to precisely describe the extent of edits. To overcome these limitations, we introduce DYG, an effective 3D drag-based editing method for 3D Gaussian Splatting. It enables users to conveniently specify the desired editing region and the desired dragging direction through the input of 3D masks and pairs of control points, thereby enabling precise control over the extent of editing. DYG integrates the strengths of the implicit triplane representation to establish the geometric scaffold of the editing results, effectively overcoming suboptimal editing outcomes caused by the sparsity of 3DGS in the desired editing regions. Additionally, we incorporate a drag-based Latent Diffusion Model into our method through the proposed Drag-SDS loss function, enabling flexible, multi-view consistent, and fine-grained editing. Extensive experiments demonstrate that DYG conducts effective drag-based editing guided by control point prompts, surpassing other baselines in terms of editing effect and quality, both qualitatively and quantitatively. Visit our project page at https://quyans.github.io/Drag-Your-Gaussian.
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