ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement

TL;DR

ReStyle introduces an iterative residual-based encoder for GAN inversion that improves accuracy and robustness with minimal additional inference time, enabling better real image manipulation.

Contribution

The paper proposes ReStyle, a novel residual-based encoder that iteratively refines latent codes for improved GAN inversion accuracy.

Findings

ReStyle outperforms existing encoder-based inversion methods.

ReStyle demonstrates robustness comparable to optimization-based methods.

Iterative refinement enhances inversion accuracy with negligible inference overhead.

Abstract

Recently, the power of unconditional image synthesis has significantly advanced through the use of Generative Adversarial Networks (GANs). The task of inverting an image into its corresponding latent code of the trained GAN is of utmost importance as it allows for the manipulation of real images, leveraging the rich semantics learned by the network. Recognizing the limitations of current inversion approaches, in this work we present a novel inversion scheme that extends current encoder-based inversion methods by introducing an iterative refinement mechanism. Instead of directly predicting the latent code of a given real image using a single pass, the encoder is tasked with predicting a residual with respect to the current estimate of the inverted latent code in a self-correcting manner. Our residual-based encoder, named ReStyle, attains improved accuracy compared to current state-of-the-art encoder-based methods with a negligible increase in inference time. We analyze the behavior of ReStyle to gain valuable insights into its iterative nature. We then evaluate the performance of our residual encoder and analyze its robustness compared to optimization-based inversion and state-of-the-art encoders.