Impressive progress in generative models and implicit representations gave rise to methods that can generate 3D shapes of high quality. However, being able to locally control and edit shapes is another essential property that can unlock several content creation applications. Local control can be achieved with part-aware models, but existing methods require 3D supervision and cannot produce textures. In this work, we devise PartNeRF, a novel part-aware generative model for editable 3D shape synthesis that does not require any explicit 3D supervision. Our model generates objects as a set of locally defined NeRFs, augmented with an affine transformation. This enables several editing operations such as applying transformations on parts, mixing parts from different objects etc. To ensure distinct, manipulable parts we enforce a hard assignment of rays to parts that makes sure that the color of each ray is only determined by a single NeRF. As a result, altering one part does not affect the appearance of the others. Evaluations on various ShapeNet categories demonstrate the ability of our model to generate editable 3D objects of improved fidelity, compared to previous part-based generative approaches that require 3D supervision or models relying on NeRFs.

翻译：生成模型与隐式表示的显著进展催生了能够生成高质量三维形状的方法。然而，局部控制与形状编辑能力是解锁诸多内容创作应用的另一关键特性。局部控制可通过部件感知模型实现，但现有方法需依赖三维监督且无法生成纹理。本文提出PartNeRF——一种新颖的部件感知生成模型，无需显式三维监督即可实现可编辑三维形状合成。该模型将对象表示为局部定义的NeRF集合，并辅以仿射变换增强。这使得多种编辑操作成为可能，例如对部件施加变换、混合不同对象的部件等。为确保部件清晰且可操控，我们强制对射线进行硬性分配，使每条射线的颜色仅由单个NeRF决定。因此，修改一个部件不会影响其他部件的外观。在多个ShapeNet类别上的评估表明，与先前需要三维监督的基于部件的生成方法或依赖NeRF的模型相比，本模型能够生成保真度更高的可编辑三维对象。