This paper tackles text-guided control of StyleGAN for editing garments in full-body human images. Existing StyleGAN-based methods suffer from handling the rich diversity of garments and body shapes and poses. We propose a framework for text-guided full-body human image synthesis via an attention-based latent code mapper, which enables more disentangled control of StyleGAN than existing mappers. Our latent code mapper adopts an attention mechanism that adaptively manipulates individual latent codes on different StyleGAN layers under text guidance. In addition, we introduce feature-space masking at inference time to avoid unwanted changes caused by text inputs. Our quantitative and qualitative evaluations reveal that our method can control generated images more faithfully to given texts than existing methods.

翻译：本文探讨了通过文本引导控制StyleGAN，以编辑全身人体图像中服装的问题。现有基于StyleGAN的方法在处理服装的丰富多样性、人体形状及姿态时存在局限。我们提出了一种基于注意力机制的潜在编码映射器框架，用于文本引导的全身人体图像合成，该框架相比现有映射器能更解耦地控制StyleGAN。我们的潜在编码映射器采用注意力机制，在文本引导下自适应地操控不同StyleGAN层上的单个潜在编码。此外，我们引入了推理时的特征空间掩码，以避免文本输入导致的非预期变化。定量与定性评估表明，与现有方法相比，我们的方法能更忠实地依据给定文本控制生成图像。