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：生成模型与隐式表征的显著进展推动了高质量三维形状生成方法的发展。然而对形状进行局部控制与编辑是实现诸多内容创作应用的关键特性，这可通过部件感知模型实现。现有方法需要三维监督且无法生成纹理。为此本文提出PartNeRF——一种无需显式三维监督的部件感知可编辑三维形状生成模型。该模型将物体生成为一组局部NeRF的集合，并辅以仿射变换增强。这支持多种编辑操作，如对部件施加变换、混合不同物体的部件等。为确保获得独立可操作的部件，我们实施射线-部件的硬分配机制，确保每条射线的颜色仅由单个NeRF决定。这使得修改一个部件不会影响其他部件的视觉表现。在多个ShapeNet类别上的评估表明，与需要三维监督的现有部件生成方法或依赖NeRF的模型相比，本模型能生成保真度更高且具备编辑能力的三维物体。