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
this https URL

我们提出了一种新颖的语义生成模型，名为 3D 语义子空间穿越器，用于特定类别 3D 形状的生成和编辑。我们的方法利用隐函数作为 3D 形状表示，并将新颖的潜在空间生成对抗网络与线性子空间模型相结合，以发现 3D 形状的本地潜在空间中的语义维度。子空间的每个维度对应于特定的语义属性，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，我们的方法能够生成具有复杂结构的合理形状，并实现语义属性的编辑。代码和训练好的模型可以在此 https URL 中获得。

3D 语义子空间遍历器：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

Generative Adversarial Networks (GAN) have emerged as a formidable AI tool to
generate realistic outputs based on training datasets. However, the challenge
of exerting control over the generation process of GANs remains a significant
hurdle. In this paper, we propose a novel methodology to address this issue by
integrating a reinforcement learning (RL) agent with a latent-space GAN
(l-GAN), thereby facilitating the generation of desired outputs. More
specifically, we have developed an actor-critic RL agent with a meticulously
designed reward policy, enabling it to acquire proficiency in navigating the
latent space of the l-GAN and generating outputs based on specified tasks. To
substantiate the efficacy of our approach, we have conducted a series of
experiments employing the MNIST dataset, including arithmetic addition as an
illustrative task. The outcomes of these experiments serve to validate our
methodology. Our pioneering integration of an RL agent with a GAN model
represents a novel advancement, holding great potential for enhancing
generative networks in the future.

通过将强化学习（RL）代理与潜在空间生成对抗网络（l-GAN）相结合，我们提出了一种新的方法来解决 GAN 生成过程中控制的挑战。我们开发了一个具备精心设计的奖励策略的 actor-critic RL 代理，使其能够在 l-GAN 的潜在空间中导航并根据指定任务生成输出，通过使用 MNIST 数据集进行了一系列实验证明了我们方法的有效性。我们首次将 RL 代理与 GAN 模型集成，具有极大的潜力来增强生成网络。