Third Time's the Charm? Image and Video Editing with StyleGAN3 (2201.13433v1)

Abstract: StyleGAN is arguably one of the most intriguing and well-studied generative models, demonstrating impressive performance in image generation, inversion, and manipulation. In this work, we explore the recent StyleGAN3 architecture, compare it to its predecessor, and investigate its unique advantages, as well as drawbacks. In particular, we demonstrate that while StyleGAN3 can be trained on unaligned data, one can still use aligned data for training, without hindering the ability to generate unaligned imagery. Next, our analysis of the disentanglement of the different latent spaces of StyleGAN3 indicates that the commonly used W/W+ spaces are more entangled than their StyleGAN2 counterparts, underscoring the benefits of using the StyleSpace for fine-grained editing. Considering image inversion, we observe that existing encoder-based techniques struggle when trained on unaligned data. We therefore propose an encoding scheme trained solely on aligned data, yet can still invert unaligned images. Finally, we introduce a novel video inversion and editing workflow that leverages the capabilities of a fine-tuned StyleGAN3 generator to reduce texture sticking and expand the field of view of the edited video.

• The paper demonstrates that StyleGAN3 effectively processes unaligned data while maintaining quality for both image and video edits.
• It reveals that the StyleSpace latent dimension provides finer control than traditional latent spaces, enabling precise image manipulation.
• A novel inversion strategy reduces texture-sticking and enhances temporal consistency in video editing, expanding its practical applications.

Image and Video Editing with StyleGAN3: An Analysis

This paper focuses on utilizing the StyleGAN3 architecture for image and video editing tasks, leveraging its distinct capabilities in handling unaligned data and enhancing image manipulation through various latent spaces. StyleGAN3 introduces notable advancements over its predecessor StyleGAN2, specifically addressing challenges prevalent in video processing, such as texture-sticking and achieving temporal consistency while retaining quality in image and video inversion.

Key Findings and Contributions

1. Handling Unaligned Data: The researchers demonstrate that StyleGAN3 can be trained with unaligned data and retain the ability to process aligned data without losing its capability to generate unaligned outputs. This flexibility is pivotal for tasks that require variability in image positioning and orientation, such as video editing.
2. Latent Space Disentanglement: The paper reports on analyzing the disentanglement of StyleGAN3's latent spaces. It is shown that the StyleSpace ($\mathcal{S}$) provides more disentangled controls compared to the $\mathcal{W}/\mathcal{W}+$ spaces. This reveals that for fine-grained editing, StyleSpace is advantageous, potentially leading to higher precision in image manipulations.
3. Image Inversion Strategy: A novel encoding scheme is proposed to invert images and process them through StyleGAN3 efficiently. The researchers highlight how encoder-based methods, trained exclusively on aligned data, can effectively invert unaligned images when paired with landmark-based transformations.
4. Video Inversion and Editing: The methodology is extended to video editing, presenting a workflow that reduces texture-sticking and enables field-of-view expansion without losing temporal coherence. This involves both latent vector smoothing and pivotal tuning inversion (PTI) for achieving enhanced fidelity in facial expressions across video frames.

Implications for Future Research

1. Enhanced Editing Capabilities in Video: The ability to edit videos using StyleGAN3 could extend to various applications beyond facial transformations, such as scenery adaptations, where temporal consistency and detail preservation are crucial.
2. Disentangled Representation Utilization: Future work could focus on applying the disentangled representations from StyleSpace for other generative tasks, potentially improving editing precision in more domains.
3. Optimized Encoder Architectures: Given the improvements observed with aligned input data, designing encoder architectures optimized for handling different poses and orientations could further refine application outputs in generative networks.

Overall, while StyleGAN3 offers substantial advantages in handling dynamic and diverse input data, there remain challenges related to its intricate latent spaces. The paper provides a detailed exploration into these areas, offering insights into practical enhancements and paving the way for future developments in the field of generative adversarial networks.