  The semantic controllability of StyleGAN is enhanced by unremitting research.
Although the existing weak supervision methods work well in manipulating the
style codes along one attribute, the accuracy of manipulating multiple
attributes is neglected. Multi-attribute representations are prone to
entanglement in the StyleGAN latent space, while sequential editing leads to
error accumulation. To address these limitations, we design a Dynamic Style
Manipulation Network (DyStyle) whose structure and parameters vary by input
samples, to perform nonlinear and adaptive manipulation of latent codes for
flexible and precise attribute control. In order to efficient and stable
optimization of the DyStyle network, we propose a Dynamic Multi-Attribute
Contrastive Learning (DmaCL) method: including dynamic multi-attribute
contrastor and dynamic multi-attribute contrastive loss, which simultaneously
disentangle a variety of attributes from the generative image and latent space
of model. As a result, our approach demonstrates fine-grained disentangled
edits along multiple numeric and binary attributes. Qualitative and
quantitative comparisons with existing style manipulation methods verify the
superiority of our method in terms of the multi-attribute control accuracy and
identity preservation without compromising photorealism.
