Consistent Style Transfer (2201.02233v1)

Abstract: Recently, attentional arbitrary style transfer methods have been proposed to achieve fine-grained results, which manipulates the point-wise similarity between content and style features for stylization. However, the attention mechanism based on feature points ignores the feature multi-manifold distribution, where each feature manifold corresponds to a semantic region in the image. Consequently, a uniform content semantic region is rendered by highly different patterns from various style semantic regions, producing inconsistent stylization results with visual artifacts. We proposed the progressive attentional manifold alignment (PAMA) to alleviate this problem, which repeatedly applies attention operations and space-aware interpolations. The attention operation rearranges style features dynamically according to the spatial distribution of content features. This makes the content and style manifolds correspond on the feature map. Then the space-aware interpolation adaptively interpolates between the corresponding content and style manifolds to increase their similarity. By gradually aligning the content manifolds to style manifolds, the proposed PAMA achieves state-of-the-art performance while avoiding the inconsistency of semantic regions. Codes are available at https://github.com/computer-vision2022/PAMA.

• The paper introduces Progressive Attentional Manifold Alignment (PAMA) to enhance semantic consistency in neural style transfer.
• It employs a multi-stage attention mechanism and space-aware interpolation to align content and style features effectively.
• Experimental results demonstrate that PAMA outperforms previous methods by delivering real-time stylization with improved visual quality.

Consistent Arbitrary Style Transfer: A Technical Analysis

The paper "Consistent Arbitrary Style Transfer" proposes an advanced methodology for improving the consistency of neural style transfer, particularly focusing on the issue of inconsistent stylization within semantic regions of images. By introducing a technique known as Progressive Attentional Manifold Alignment (PAMA), the authors address the limitations of previous methods, notably those that rely heavily on per-point transformations without considering the manifold structure of image features.

Overview of Methodology

The core contribution of this work is the development of PAMA, which involves a multi-step alignment process aimed at ensuring that features from content and style images are consistently matched across semantic regions. Unlike prior approaches that focus mainly on point-wise feature similarity, PAMA accounts for the multi-manifold distribution of features, where each manifold is associated with different semantic regions in the image.

1. Attention Mechanism: The paper expands on traditional attention mechanisms by facilitating manifold correspondence between content and style features. It rearranges the style features to match the spatial structure of the content features, ensuring semantic alignment at the level of manifolds.
2. Space-Aware Interpolation: After achieving manifold alignment, the space-aware interpolation process enhances the similarity between corresponding features. This is done through dynamic interpolation adapted to the feature manifolds, thereby reinforcing the consistency of stylization across different regions.
3. Multistage Process: The alignment process is executed over multiple stages to gradually improve feature correspondence. This staged approach is supported by a carefully constructed loss function framework that includes structure self-similarity, relaxed earth mover distance (REMD), and moment matching losses.

This method integrates the advantages of manifold alignment theories with attention mechanisms to produce stylized images that retain structural coherence without sacrificing style expressivity.

Experimental Results and Implications

The paper demonstrates that PAMA significantly outperforms existing methods such as AdaIN, SANet, and MAST in terms of regional consistency, style quality, and overall visual appeal. With running times of 101 frames per second for 512px images on a Tesla V100 GPU, the proposed method is also efficient, providing real-time performance critical for practical applications in creative and multimedia industries.

Theoretical Implications:

• The integration of manifold alignment with attention mechanisms represents a novel architectural advance, showcasing the potential for improved feature correspondence in neural networks.
• The use of multistage loss functions suggests a new avenue for training deep networks with iterative refinement, particularly pertinent for tasks requiring high fidelity in stylistic transformations.

Practical Implications:

• The ability to produce more consistent and visually appealing stylizations has direct applications in digital content creation, where maintaining the integrity of semantic content during stylization is crucial.
• Real-time performance enables applications in live video processing, enhancing creative possibilities in artistic disciplines and interactive media.

Future Directions

The research opens up several pathways for future exploration, including:

• Extending the PAMA framework to handle more complex scenes with multiple interacting objects and subtle semantic boundaries.
• Exploring the integration of this methodology with generative models for automatic content generation.
• Investigating potential applications in domains beyond image stylization, such as semantic segmentation and image editing, where similar consistency challenges are prevalent.

In conclusion, this paper provides a novel contribution to the field of style transfer by addressing a fundamental issue of semantic consistency through a unique combination of manifold alignment and attention mechanisms. The practical and theoretical advancements presented set a foundation for further research and development in consistent image stylization techniques.