Shape generation is the practice of producing 3D shapes as various representations for 3D content creation. Previous studies on 3D shape generation have focused on shape quality and structure, without or less considering the importance of semantic information. Consequently, such generative models often fail to preserve the semantic consistency of shape structure or enable manipulation of the semantic attributes of shapes during generation. In this paper, we proposed a novel semantic generative model named 3D Semantic Subspace Traverser that utilizes semantic attributes for category-specific 3D shape generation and editing. Our method utilizes implicit functions as the 3D shape representation and combines a novel latent-space GAN with a linear subspace model to discover semantic dimensions in the local latent space of 3D shapes. Each dimension of the subspace corresponds to a particular semantic attribute, and we can edit the attributes of generated shapes by traversing the coefficients of those dimensions. Experimental results demonstrate that our method can produce plausible shapes with complex structures and enable the editing of semantic attributes. The code and trained models are available at https://github.com/TrepangCat/3D_Semantic_Subspace_Traverser

翻译：形状生成是生成三维形状以进行三维内容创作的实践。以往关于三维形状生成的研究主要关注形状质量和结构，较少或未考虑语义信息的重要性。因此，这类生成模型往往无法保持形状结构的语义一致性，也无法在生成过程中操纵形状的语义属性。本文提出了一种名为3D语义子空间遍历器的新型语义生成模型，该模型利用语义属性进行类别特定的三维形状生成与编辑。我们的方法采用隐式函数作为三维形状表示，并结合一种新颖的潜空间生成对抗网络与线性子空间模型，以发现三维形状局部潜空间中的语义维度。子空间的每个维度对应一个特定的语义属性，通过遍历这些维度的系数，我们可以编辑生成形状的属性。实验结果表明，我们的方法能够生成具有复杂结构的合理形状，并实现对语义属性的编辑。代码和训练好的模型可在 https://github.com/TrepangCat/3D_Semantic_Subspace_Traverser 获取。