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Shape generation is the practice of producing 3D shapes as variousrepresentations for 3D content creation. Previous studies on 3D shapegeneration have focused on shape quality and structure, without or lessconsidering the importance of semantic information. Consequently, suchgenerative models often fail to preserve the semantic consistency of shapestructure or enable manipulation of the semantic attributes of shapes duringgeneration. In this paper, we proposed a novel semantic generative model named3D Semantic Subspace Traverser that utilizes semantic attributes forcategory-specific 3D shape generation and editing. Our method utilizes implicitfunctions as the 3D shape representation and combines a novel latent-space GANwith a linear subspace model to discover semantic dimensions in the locallatent space of 3D shapes. Each dimension of the subspace corresponds to aparticular semantic attribute, and we can edit the attributes of generatedshapes by traversing the coefficients of those dimensions. Experimental resultsdemonstrate that our method can produce plausible shapes with complexstructures and enable the editing of semantic attributes. The code and trainedmodels are available athttps://github.com/TrepangCat/3D_Semantic_Subspace_Traverser

3D Semantic Subspace Traverser: Empowering 3D Generative Model with Shape Editing Capability