Learning deep generative models for 3D shape synthesis is largely limited by the difficulty of generating plausible shapes with correct topology and reasonable geometry. Indeed, learning the distribution of plausible 3D shapes seems a daunting task for most existing holistic shape representation, given the significant topological variations of 3D objects even within the same shape category. Enlightened by the common view that 3D shape structure is characterized as part composition and placement, we propose to model 3D shape variations with a \emph{part-aware} deep generative network which we call \emph{PAGENet}. The network is composed of an array of per-part VAE-GANs, generating semantic parts composing a complete shape, followed by a part assembly module that estimates a transformation for each part to correlate and assemble them into a plausible structure. Through splitting the generation of part composition and part relations into separate networks, the difficulty of modeling structural variations of 3D shapes is greatly reduced. We demonstrate through extensive experiments that PARANet generates 3D shapes with plausible, diverse and detailed structure, and show two prototype applications: semantic shape segmentation and shape set evolution.
翻译:3D 形状合成的深层基因化模型主要由于难以生成具有正确地形学和合理几何特征的貌似形状而基本受到限制。事实上,了解可信的3D形状的分布对于大多数现有整体形状代表来说似乎是一项艰巨的任务,因为即使在同一形状类别中,3D对象的地形变化也很大。 3D形状结构被3D形状结构定性为构成和布置的一部分这一共同观点启迪了我们提议模型3D形状变异,我们称之为\emph{part-aware}深层基因化网络。通过广泛的实验,我们证明PARANet生成了3D形状,结构合理、多样和详细,并展示了两种原型应用:形状的形状分解和形状的进化。