CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer (2207.04808v4)

Abstract: In this paper, we aim to devise a universally versatile style transfer method capable of performing artistic, photo-realistic, and video style transfer jointly, without seeing videos during training. Previous single-frame methods assume a strong constraint on the whole image to maintain temporal consistency, which could be violated in many cases. Instead, we make a mild and reasonable assumption that global inconsistency is dominated by local inconsistencies and devise a generic Contrastive Coherence Preserving Loss (CCPL) applied to local patches. CCPL can preserve the coherence of the content source during style transfer without degrading stylization. Moreover, it owns a neighbor-regulating mechanism, resulting in a vast reduction of local distortions and considerable visual quality improvement. Aside from its superior performance on versatile style transfer, it can be easily extended to other tasks, such as image-to-image translation. Besides, to better fuse content and style features, we propose Simple Covariance Transformation (SCT) to effectively align second-order statistics of the content feature with the style feature. Experiments demonstrate the effectiveness of the resulting model for versatile style transfer, when armed with CCPL.

• The paper’s main contribution is the introduction of CCPL, which preserves local content coherence to boost temporal consistency in style transfer.
• It presents a Simple Covariance Transformation (SCT) module that efficiently aligns second-order statistics for seamless style fusion.
• Experimental results demonstrate lower SIFID and LPIPS scores, outperforming traditional methods by reducing local distortions and enhancing visual quality.

Contrastive Coherence Preserving Loss for Versatile Style Transfer: A Professional Overview

The paper "CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer" introduces an innovative approach to style transfer that emphasizes versatility across artistic, photo-realistic, and video domains. The method outlined does not rely on video data during training but still manages to ensure temporal consistency in stylized videos—an achievement previous single-frame methods struggled with due to their reliance on global image constraints.

Core Contributions and Methodology

The central contribution of this work is the novel Contrastive Coherence Preserving Loss (CCPL), which operates on local image patches rather than entire images. This approach assumes that global image inconsistencies largely arise from local inconsistencies. Thus, by focusing on preserving content coherence at the patch level, the method enhances temporal consistency without diminishing stylistic transformation.

In addition to CCPL, the paper proposes a Simple Covariance Transformation (SCT) module aimed at efficiently aligning the second-order statistics of content and style features. SCT facilitates the fusion of these features within the style transfer network, named SCTNet. This network is characterized by a lightweight architecture that achieves high frame rates, making it practical for real-time applications.

Numerical Results and Experimental Validation

Quantitative analysis demonstrates that the use of CCPL significantly improves temporal consistency metrics, including short-term and long-term frame coherence, while maintaining competitive stylization quality. The results show that the CCPL approach can effectively reduce local distortions and enhance visual quality—a marked improvement over competing methods such as AdaIN and SANet when integrated with CCPL.

The evaluation utilizes various performance metrics, including SIFID to measure style distribution closeness and LPIPS for assessing visual similarity. The proposed method achieves lower SIFID scores, indicating a closer adherence to target style distribution, and demonstrates marked improvements in LPIPS, reflecting enhanced temporal coherence across frames.

Theoretical Implications and Flexibility

The contrastive learning framework leveraged in CCPL highlights the potential for cross-domain application of contrastive loss schemes beyond standard visual representation learning tasks. The adaptability of CCPL, as evidenced by its successful application to existing style transfer networks like Linear and MCCNet, illustrates its potential for broader integration into image-to-image translation tasks.

Speculations on the Future of AI in Style Transfer

Anticipating further developments, the application of CCPL could be expanded to include more complex temporal dynamics and multi-modal content sources, potentially extending its utility in areas such as video editing and augmented reality. The focus on local patch coherence suggests a promising direction for future work, where dynamic and adaptive patch sizes or attention mechanisms could be explored to further refine temporal consistency and visual fidelity.

Conclusion

In summary, the paper adeptly balances the demands of temporal consistency and visual stylization through a localized approach to coherence preservation. The introduction of CCPL and its integration into versatile style transfer networks establishes a new benchmark for cohesive style transfer in video and image domains, paving the way for future explorations that can capitalize on its core principles. The combination of theoretical innovation and practical efficiency marks this contribution as significant within the field of neural style transfer.