  Recent works have shown that a rich set of semantic directions exist in the
latent space of Generative Adversarial Networks (GANs), which enables various
facial attribute editing applications. However, existing methods may suffer
poor attribute variation disentanglement, leading to unwanted change of other
attributes when altering the desired one. The semantic directions used by
existing methods are at attribute level, which are difficult to model complex
attribute correlations, especially in the presence of attribute distribution
bias in GAN's training set. In this paper, we propose a novel framework (IALS)
that performs Instance-Aware Latent-Space Search to find semantic directions
for disentangled attribute editing. The instance information is injected by
leveraging the supervision from a set of attribute classifiers evaluated on the
input images. We further propose a Disentanglement-Transformation (DT) metric
to quantify the attribute transformation and disentanglement efficacy and find
the optimal control factor between attribute-level and instance-specific
directions based on it. Experimental results on both GAN-generated and
real-world images collectively show that our method outperforms
state-of-the-art methods proposed recently by a wide margin. Code is available
at https://github.com/yxuhan/IALS.
