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：形状生成是产生3D形状作为3D内容创作多种表示形式的实践。以往关于3D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，此类生成模型往往无法在生成过程中保持形状结构的语义一致性，或无法对形状的语义属性进行操作。本文提出了一种新颖的语义生成模型——3D语义子空间遍历器，该模型利用语义属性进行类别特定的3D形状生成与编辑。我们的方法采用隐式函数作为3D形状表示，并将新颖的潜空间生成对抗网络与线性子空间模型相结合，以发现3D形状局部潜空间中的语义维度。子空间的每个维度对应特定的语义属性，通过遍历这些维度的系数，即可编辑生成形状的属性。实验结果表明，我们的方法能够生成结构复杂的合理形状，并支持语义属性的编辑。相关代码与训练模型已开源至 https://github.com/TrepangCat/3D_Semantic_Subspace_Traverser