StyleHumanCLIP: Text-guided Garment Manipulation for StyleGAN-Human
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 42 of its 52 references that are in the map. First posted on arXiv 26 May 2023.
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42 of 52 references.
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