3D Semantic Subspace Traverser：赋予3D生成模型形状编辑能力
3D Semantic Subspace Traverser: Empowering 3D Generative Model with Shape Editing Capability
AI总结：
本文提出3D Semantic Subspace Traverser，通过潜空间GAN与线性子空间模型识别语义维度，实现特定类别3D形状的高质量生成与可控语义编辑。
摘要：
形状生成是指将3D形状生成为各种表示形式，以用于3D内容创作的实践。以往关于3D形状生成的研究关注形状质量和结构，却没有或较少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控形状的语义属性。本文提出了一种名为3D Semantic Subspace Traverser的新型语义生成模型，利用语义属性进行特定类别的3D形状生成与编辑。该方法使用隐式函数作为3D形状表示，并将一种新型潜空间GAN与线性子空间模型相结合，以在3D形状的局部潜空间中发现语义维度。子空间的每个维度对应一个特定语义属性，我们可以通过遍历这些维度的系数来编辑生成形状的属性。实验结果表明，该方法能够生成具有复杂结构的合理形状，并支持语义属性编辑。代码和训练模型可在https://github.com/TrepangCat/3D_Semantic_Subspace_Traverser获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
