标题： 3D-LATTE：从文本指令中进行潜在空间的3D编辑 显示英文标题

标题： 3D-LATTE: Latent Space 3D Editing from Textual Instructions

摘要： 尽管多视角扩散模型在基于文本/图像的3D资产生成方面取得了最近的成功，但基于指令的3D资产编辑却远远落后于生成模型的质量。 主要原因在于，使用2D先验的近期方法会受到视角不一致的编辑信号的影响。 超越2D先验蒸馏方法和多视角编辑策略，我们提出了一种无需训练的编辑方法，在原生3D扩散模型的潜在空间内操作，使我们能够直接操纵3D几何体。 我们通过将生成过程中的3D注意力图与源对象进行融合来引导编辑合成。 结合几何感知的正则化引导、傅里叶域中的频谱调制策略以及用于3D增强的细化步骤，我们的方法优于之前的3D编辑方法，在各种形状和语义操作上实现了高保真、精确和鲁棒的编辑。 显示英文摘要

摘要： Despite the recent success of multi-view diffusion models for text/image-based 3D asset generation, instruction-based editing of 3D assets lacks surprisingly far behind the quality of generation models. The main reason is that recent approaches using 2D priors suffer from view-inconsistent editing signals. Going beyond 2D prior distillation methods and multi-view editing strategies, we propose a training-free editing method that operates within the latent space of a native 3D diffusion model, allowing us to directly manipulate 3D geometry. We guide the edit synthesis by blending 3D attention maps from the generation with the source object. Coupled with geometry-aware regularization guidance, a spectral modulation strategy in the Fourier domain and a refinement step for 3D enhancement, our method outperforms previous 3D editing methods enabling high-fidelity, precise, and robust edits across a wide range of shapes and semantic manipulations.