StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN (2403.14186v1)

Abstract: We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent unconditional video generation, we leverage a powerful pre-trained image generator to synthesize high-quality cinemagraphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN, our approach utilizes its deep feature space for both GAN inversion and cinemagraph generation. Specifically, we propose multi-scale deep feature warping (MSDFW), which warps the intermediate features of a pre-trained StyleGAN at different resolutions. By using MSDFW, the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.

• The paper presents a novel method leveraging a pre-trained StyleGAN for high-resolution cinemagraph generation in landscape images.
• It employs GAN inversion with deep feature extraction, mask prediction, and multi-scale warping to accurately separate motion from static areas.
• User studies and comparisons demonstrate enhanced visual quality and computational efficiency over existing automated cinemagraph techniques.

StyleCineGAN: Automating Cinemagraph Creation from Landscape Images with Pre-trained StyleGAN

Introduction

The creation of cinemagraphs, which are photographic animations where a minor and repeated movement occurs within a still image, typically demands considerable manual effort and expertise. However, the newly presented StyleCineGAN approach aims to automate this process, specifically for landscape images, by exploiting a pre-trained StyleGAN architecture. Unlike its predecessors, which either depend on manual editing or training deep generative models from scratch, StyleCineGAN introduces a novel utilization of deep feature space from a pre-trained StyleGAN for both GAN inversion and cinemagraph generation, paving the way for high-resolution (1024x1024) cinemagraph creation with reduced computational demands and time.

Related Work

Prior attempts at automatic cinemagraph creation have explored a variety of methods ranging from manual editing techniques to leveraging reference videos for guidance. Recent approaches have made strides with deep learning, training models to capture motion and spatial information from separate datasets but often at the expense of resolution due to the intensive computational requirements. In contrast, unconditional video generation techniques utilizing pre-trained image generators like StyleGAN have demonstrated potential in navigating latent spaces to synthesize videos but at the sacrifice of content preservation, creating a gap that StyleCineGAN aims to fill.

Methodology

StyleCineGAN differentiates itself by employing the deep features of StyleGAN, allowing for the precise reconstruction of high-quality images and the generation of plausible motion. Notable components of the method include:

• GAN Inversion: Enhanced by projecting images into both the latent and feature spaces of StyleGAN, improving upon traditional methods by capturing the fine details of landscape images more accurately.
• Mask Prediction: Utilizing deep features to predict segmentation masks, distinguishing between the static and dynamic regions to maintain the integrity of unmoving aspects of the scene.
• Motion Generation: Employing a motion generator to predict an Eulerian motion field, dictating the movement within the cinemagraph and refined through segmentation masks for clearer distinction between moving and stationary elements.
• Cinemagraph Synthesis: The core innovation lies in the Multi-Scale Deep Feature Warping (MSDFW), which applies predicted motions at multiple resolutions in StyleGAN's deep feature space, facilitated by forward warping to minimize distortion and artifacts.

Evaluation and Results

Comparative analyses against state-of-the-art methods in both cinemagraph generation and unconditional video synthesis validated StyleCineGAN's superiority in generating visually pleasing and high-resolution cinemagraphs. User studies further endorsed these findings, emphasizing the enhanced static consistency and motion quality StyleCineGAN offers.

Limitations and Future Directions

While StyleCineGAN marks a significant advancement in automated cinemagraph creation, limitations exist. The motion prediction can be imprecise for complex scenes, and thin structures within the animated areas may not be adequately isolated. Extending the scope beyond landscape images to incorporate varied types of motion presents a promising avenue for future research.

Conclusion

StyleCineGAN introduces a groundbreaking methodology for automatically generating high-resolution cinemagraphs by leveraging a pre-trained StyleGAN. It addresses the challenges of content preservation and resolution limitations prevalent in existing methods, offering a path toward more accessible creation of cinemagraphs without compromising on quality. Its implications extend beyond cinemagraph creation, potentially influencing future developments in AI-driven art and animation.