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.

本文提出了一种基于文本引导的全身人像图像合成框架，采用基于注意力机制的潜在代码映射器，实现了比现有映射器更多元化对 StyleGAN 的控制，并在推理时引入特征空间掩码机制来避免因文本输入导致的不良影响，经定量和定性评估，证明了我们的方法比现有方法更能忠实地控制生成的图像。