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.
翻译：不加释放的研究增强了SdleGAN 的语义控制能力。 尽管现有的薄弱监督方法在调用一个属性的样式代码时效果良好,但操纵多属性的准确性却被忽视。 多属性表示在StuGAN 潜在空间中容易被纠缠,而顺序编辑则导致累积错误。为了解决这些限制,我们设计了一个动态样式管理网络(DyStle),其结构和参数因输入样本而不同,其结构和参数因输入样本而不同,以对灵活和精确的属性控制的潜在代码进行非线性和适应性地调整,进行不线性和适应性地对隐性代码进行非线性和适应性操纵。虽然现有的监管系统优化效果和稳定地优化DyStySty的网络,但我们建议一种动态多属性分布式的多属性差异处理(DmaCL)方法:包括动态多属性对比器和动态多分布式多分布式多属性分布式编辑器和多属性和动态多属性对比性编辑制损失,同时将各种属性图像图像图像图像图像图像图像图像和模型术语核查我们方法的各种属性的各种属性。因此,我们的方法展示了精细分分解的分解分解分解分解的编辑和分解的编辑和定量比较。多级比较和定量和定量比较与现有风格、多级比较和定量比较与多级、多级、多级调制的多级比较与多级比较,用、制制制制制制制制制制的多级的多级、多级比较,用、多级、多级、多级、多级、多级比较,用制制制制制制制制制制制制制制制制制制制制制制制制制制制制制制制制制制制制制的多,用方法,用方法,用方法用方法用,用、制、多制制制的多,用、制、制制制制制制、制、制、制、制、制、制、制、制、多制、多制、多制、多制、多制、多制、多制、多制、多制、多制制制、制、多制、多制、多制、多制、多制、多制、多制、多制、多制、多制、多制、多
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