通过语义保留约束和伪配对监督推进面部风格化
Advancing Facial Stylization through Semantic Preservation Constraint and Pseudo-Paired Supervision
AI总结：
提出整合语义保留约束与伪配对监督的面部风格化方法，解决生成器语义偏移问题，实现高保真且灵活的多模态风格迁移。
摘要：
面部风格化旨在将面部图像转换为具有吸引力的高质量风格化肖像，其关键挑战在于准确学习目标风格的同时保持与原始图像的内容一致性。尽管以往基于StyleGAN的方法取得了显著进展，但生成结果仍存在伪影或对源图像的保真度不足。我们认为这些问题源于忽视了风格化过程中生成器的语义偏移。因此，我们提出一种面部风格化方法，整合语义保留约束和伪配对监督，以增强内容对应性并改善风格化效果。此外，我们开发了一种创建多级伪配对数据集的方法来实现监督约束。进一步地，基于我们的面部风格化框架，我们在无需复杂网络架构设计或额外训练的情况下，实现了更灵活的多模态和参考引导的风格化。实验结果表明，我们的方法产生了高保真、美观的面部风格迁移，超越了以往的方法。
英文摘要：
Facial stylization aims to transform facial images into appealing, high-quality stylized portraits, with the critical challenge of accurately learning the target style while maintaining content consistency with the original image. Although previous StyleGAN-based methods have made significant advancements, the generated results still suffer from artifacts or insufficient fidelity to the source image. We argue that these issues stem from neglecting semantic shift of the generator during stylization. Therefore, we propose a facial stylization method that integrates semantic preservation constraint and pseudo-paired supervision to enhance the content correspondence and improve the stylization effect. Additionally, we develop a methodology for creating multi-level pseudo-paired datasets to implement supervisory constraint. Furthermore, building upon our facial stylization framework, we achieve more flexible multimodal and reference-guided stylization without complex network architecture designs or additional training. Experimental results demonstrate that our approach produces high-fidelity, aesthetically pleasing facial style transfer that surpasses previous methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
