Region-Based Semantic Factorization in GANs
This addresses the need for fine-grained image editing in generative models, offering a more efficient and user-friendly solution compared to prior methods.
The paper tackles the problem of local semantic manipulation in GANs by proposing an efficient algorithm for region-based semantic factorization, achieving precise control and robustness without annotations or training.
Despite the rapid advancement of semantic discovery in the latent space of Generative Adversarial Networks (GANs), existing approaches either are limited to finding global attributes or rely on a number of segmentation masks to identify local attributes. In this work, we present a highly efficient algorithm to factorize the latent semantics learned by GANs concerning an arbitrary image region. Concretely, we revisit the task of local manipulation with pre-trained GANs and formulate region-based semantic discovery as a dual optimization problem. Through an appropriately defined generalized Rayleigh quotient, we manage to solve such a problem without any annotations or training. Experimental results on various state-of-the-art GAN models demonstrate the effectiveness of our approach, as well as its superiority over prior arts regarding precise control, region robustness, speed of implementation, and simplicity of use.