  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
