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:基因模型和隐含表达方式的显著进步导致产生3D高质量形状的方法。 但是, 能够本地控制和编辑形状是另一个重要属性, 可以解开多个内容创建应用程序。 局部控制可以使用部分认知模型实现, 但现有方法需要3D监督, 无法产生纹理。 在这项工作中, 我们设计了 PartNeRF, 这是用于编辑3D 形状合成的新颖的部分认知模型, 不需要任何明确的 3D 监督。 我们的模型生成了由本地定义的一组 NeRF 对象, 并辅之以一个直线转换。 这使一些编辑操作得以进行, 如对部件应用转换, 混合不同对象的部件等等。 为确保不同、 可调制的部件, 我们强制对部分进行硬性射线分配, 以确保每个射线的颜色只由单一的 NERF 来决定。 结果, 部分的改变并不影响其它部分的外观。 对各种形状网络类别的评估表明, 我们的模型能够生成可编辑的3D改进的忠诚对象, 与以前的基于前部分的基因分析模型相比, 需要 3D 或依赖3D 模型的模型。</s>