GAN inversion and editing via StyleGAN maps an input image into the embedding spaces ($\mathcal{ W}$, $\mathcal{ W^+}$, and $\mathcal{ F}$) to simultaneously maintain image fidelity and meaningful manipulation. From latent space $\mathcal{ W}$ to extended latent space $\mathcal{ W^+}$ to feature space $\mathcal{ F}$ in StyleGAN, the editability of GAN inversion decreases while its reconstruction quality increases. Recent GAN inversion methods typically explore $\mathcal{ W^+}$ and $\mathcal{ F}$ rather than $\mathcal{ W}$ to improve reconstruction fidelity while maintaining editability. As $\mathcal{ W^+}$ and $\mathcal{ F}$ are derived from $\mathcal{ W}$ that is essentially the foundation latent space of StyleGAN, these GAN inversion methods focusing on $\mathcal{ W^+}$ and $\mathcal{ F}$ spaces could be improved by stepping back to $\mathcal{ W}$. In this work, we propose to first obtain the precise latent code in foundation latent space $\mathcal{ W}$. We introduce contrastive learning to align $\mathcal{ W}$ and the image space for precise latent code discovery. %The obtaining process is by using contrastive learning to align $\mathcal{ W}$ and the image space. Then, we leverage a cross-attention encoder to transform the obtained latent code in $\mathcal{ W}$ into $\mathcal{ W^+}$ and $\mathcal{ F}$, accordingly. Our experiments show that our exploration of the foundation latent space $\mathcal{ W}$ improves the representation ability of latent codes in $\mathcal{ W^+}$ and features in $\mathcal{ F}$, which yields state-of-the-art reconstruction fidelity and editability results on the standard benchmarks. Project page: https://kumapowerliu.github.io/CLCAE.
Categories: cs.CV