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

作者：Konstantinos Tertikas, Pascalidou Despoina, Boxiao Pan, J. Park, M. Uy, I. Emiris, Yannis Avrithis, L. Guibas · 发表于：Computer Vision and Pattern Recognition · 年份：2023 · DOI：10.1109/cvpr52729.2023.00434 · 被引用次数：45 · 研究领域：Computer Science

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 con-trol 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 meth-ods require 3D supervision and cannot produce textures. In this work, we devise PartNeRF, a novel part-aware gener-ative 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 op-erations such as applying transformations on parts, mixing parts from different objects etc. To ensure distinct, manip-ulable 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 af-fect 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.