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PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervision

2023-03-16 17:59:22
Konstantinos Tertikas, Pascalidou Despoina, Boxiao Pan, Jeong Joon Park, Mikaela Angelina Uy, Ioannis Emiris, Yannis Avrithis, Leonidas Guibas


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.

Abstract (translated)

在生成模型和隐含表示方面的出色进展催生了生成高质量3D形状的方法。然而,能够对形状进行局部控制和编辑也是解锁多个内容创建应用的另一个关键特性。局部控制可以使用部分感知模型来实现,但现有的方法需要3D监督,无法产生纹理。在本文中,我们设计了一种名为PartNeRF的部分感知生成模型,它是一种全新的可编辑3D形状合成模型,不需要任何明确的3D监督。我们的模型生成对象 as a set of locally defined NeRFs,加上一个平移变换。这使可以进行多个编辑操作,例如对部分应用变换,从不同对象混合部分等。为了确保具有可操纵的部分,我们强制将光线分配给部分,以确保每个光线的颜色仅由一个NeRF决定。因此,改变一个部分不会影响其他部分的外观。在各种形状Net类别上的评估表明,我们的模型能够生成改进细节的可编辑3D对象,与之前需要3D监督或依靠NeRF的基于部分生成方法相比。



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