A Closed-form Solution to Photorealistic Image Stylization (1802.06474v5)

Abstract: Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic. While several photorealistic image stylization methods exist, they tend to generate spatially inconsistent stylizations with noticeable artifacts. In this paper, we propose a method to address these issues. The proposed method consists of a stylization step and a smoothing step. While the stylization step transfers the style of the reference photo to the content photo, the smoothing step ensures spatially consistent stylizations. Each of the steps has a closed-form solution and can be computed efficiently. We conduct extensive experimental validations. The results show that the proposed method generates photorealistic stylization outputs that are more preferred by human subjects as compared to those by the competing methods while running much faster. Source code and additional results are available at https://github.com/NVIDIA/FastPhotoStyle .

• The paper presents a closed-form solution that integrates a stylization step using WCT with enhanced unpooling to preserve spatial details.
• It employs a smoothing step based on a manifold ranking algorithm to ensure uniform style application and minimize artifacts.
• Experimental results demonstrate improved photorealism and consistent user preference over traditional stylization techniques.

A Closed-form Solution to Photorealistic Image Stylization

The paper presents a novel method for photorealistic image stylization, addressing challenges in maintaining spatial consistency and minimizing artifacts while transferring style between images. The authors propose a two-step approach leveraging a stylization step and a smoothing step, both with closed-form solutions, enhancing efficiency and output quality over existing techniques.

Methodology Overview

1. Stylization Step: The stylization utilizes the Whitening and Coloring Transform (WCT) to project features, effectively adapting the technique for photorealistic stylization. Enhancements include replacing upsampling layers with unpooling layers to preserve detailed spatial information. This adaptation is inspired by deficiencies observed in previous artistic stylization applications when applied to photorealism.
2. Smoothing Step: A photorealistic smoothing function is employed to ensure uniform style application across semantically similar regions. The smoothing utilizes a manifold ranking algorithm to derive affinities from the content image, solving a quadratic optimization problem, yielding a closed-form solution that efficiently refines the stylization.

Experimental Results

The method outputs results that maintain strong photorealistic properties while showing improved stylistic transfer compared to competing methods, such as those by Reinhard et al. and Luan et al. The experiments demonstrate consistent user preference for the proposed method in terms of both stylization effect and realism. The approach significantly reduces noticeable artifacts and inconsistencies seen in competing methods.

Comparisons and Benchmarking

Several comparative studies show that the proposed method achieves superior results rapidly:

• Stylization Comparison: The paper competes against both classical and modern neural-based stylization methods. It demonstrates significant improvements, particularly in scenarios involving complex style transfers (e.g., seasonal or time-of-day transformations).
• User Studies: Report a clear preference for this method's outputs over others, highlighting improvements in maintaining content structure while transferring style.

Future Implications and Research Directions

The proposed framework opens new avenues in developing photorealistic style transfer methodologies that could enhance various applications such as cinematic post-processing, virtual reality environments, and creative digital content creation. Future work could further refine the algorithm's speed while maintaining or improving quality, particularly through innovations in affinity approximation or real-time implementations.

In conclusion, this paper contributes a substantial improvement in the efficiency and accuracy of photorealistic image stylization, holding potential for broad impact in artificial intelligence, specifically in domains intersecting computer vision, graphics, and creative technology applications.