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.
翻译:文本到图像扩散模型正在逐渐引入计算机图形学中,最近使得开放域的文本到 3D 管道得以开发。然而,对于交互式编辑目的,通过简单的文本界面进行局部内容操作可能会很困难。将用户引导草图与文本到图像管道结合起来,为用户提供更直观的控制。但是,由于最先进的文本到 3D 管道依赖于通过任意渲染视图的梯度来优化神经辐射场(NeRF),因此对草图的条件限制并不直观。在本文中,我们提出了 SKED,一种用于编辑由 NeRF 表示的 3D 形状的技术。我们的技术使用来自不同视角的至少两个引导草图来改变现有的神经场。通过预训练的扩散模型,编辑区域遵循提示语义。为确保生成的输出符合提供的草图,我们提出了新颖的损失函数,以生成所需的编辑,同时保留基础实例的密度和辐射。我们通过多个定性和定量实验证明了我们提出的方法的有效性。