Instance-level facial attribute transfer aims at transferring an attribute including its style from a source face to a target one. Existing studies have limitations on fidelity or correctness. To address this problem, we propose a weakly supervised style disentangling method embedded in Generative Adversarial Network (GAN) for accurate instance-level attribute transfer, using only binary attribute annotations. In our method, the whole attributes transfer process is designed as two steps for easier transfer, which first removes the original attribute or transfers it to a neutral state and then adds the attributes style disentangled from a source face. Moreover, a style disentangling module is proposed to extract the attribute style of an image used in the adding step. Our method aims for accurate attribute style transfer. However, it is also capable of semantic attribute editing as a special case, which is not achievable with existing instance-level attribute transfer methods. Comprehensive experiments on CelebA Dataset show that our method can transfer the style more precisely than existing methods, with an improvement of 39% in user study, 16.5% in accuracy, and about 3.3 in FID.

实例级面部属性传输旨在将包括其样式的属性从源面部转移到目标面部。现有研究在保真度或正确性方面存在局限性。为了解决这个问题，我们提出了一种嵌入在生成对抗网络（GAN）中的弱监督样式分解方法，该方法仅使用二进制属性注释即可进行准确的实例级属性传递。在我们的方法中，整个属性传递过程被设计为两个步骤，以简化传递过程，该过程首先删除原始属性或将其传递到中立状态，然后添加从源面解开的属性样式。此外，提出了一种样式分解模块，以提取添加步骤中使用的图像的属性样式。我们的方法旨在准确地传递属性样式。然而，作为特殊情况，它还能够进行语义属性编辑，这是现有实例级属性传输方法无法实现的。在CelebA数据集上进行的综合实验表明，我们的方法可以比现有方法更准确地传递样式，在用户研究中提高了39％，在准确性方面提高了16.5％，在FID中提高了约3.3。