Text-to-image diffusion models are gradually introduced into computer graphics, recently enabling the development of Text-to-3D pipelines in an open domain. However, for interactive editing purposes, local manipulations of content through a simplistic textual interface can be arduous. Incorporating user guided sketches with Text-to-image pipelines offers users more intuitive control. Still, as state-of-the-art Text-to-3D pipelines rely on optimizing Neural Radiance Fields (NeRF) through gradients from arbitrary rendering views, conditioning on sketches is not straightforward. In this paper, we present SKED, a technique for editing 3D shapes represented by NeRFs. Our technique utilizes as few as two guiding sketches from different views to alter an existing neural field. The edited region respects the prompt semantics through a pre-trained diffusion model. To ensure the generated output adheres to the provided sketches, we propose novel loss functions to generate the desired edits while preserving the density and radiance of the base instance. We demonstrate the effectiveness of our proposed method through several qualitative and quantitative experiments. https://sked-paper.github.io/

翻译：文本到图像扩散模型正逐步引入计算机图形学领域，近期推动了开放域文本到三维管线的开发。然而，在交互式编辑应用中，通过简单的文本界面进行局部内容操作较为困难。将用户引导的草图与文本到图像管线结合，能为用户提供更直观的控制方式。但当前最先进的文本到三维管线依赖于通过任意渲染视角的梯度优化神经辐射场，这使得基于草图的约束条件难以直接实现。本文提出SKED技术，用于编辑由神经辐射场表示的三维形状。该技术仅需利用不同视角的两个引导草图即可修改现有神经场，编辑区域通过预训练扩散模型遵循提示语义。为确保生成输出符合所提供的草图，我们提出了新颖的损失函数，在保留基础实例密度与辐射度的同时生成所需编辑。通过多项定性与定量实验，我们验证了所提方法的有效性。https://sked-paper.github.io/