Title: Real Time Animator: High-Quality Cartoon Style Transfer in 6 Animation Styles on Images and Videos Show Chinese title

Title: 实时动画师：图像和视频上六种动画风格的高质量卡通风格迁移

Abstract: This paper presents a comprehensive pipeline that integrates state-of-the-art techniques to achieve high-quality cartoon style transfer for educational images and videos. The proposed approach combines the Inversion-based Style Transfer (InST) framework for both image and video style stylization, the Pre-Trained Image Processing Transformer (IPT) for post-denoising, and the Domain-Calibrated Translation Network (DCT-Net) for more consistent video style transfer. By fine-tuning InST with specific cartoon styles, applying IPT for artifact reduction, and leveraging DCT-Net for temporal consistency, the pipeline generates visually appealing and educationally effective stylized content. Extensive experiments and evaluations using the scenery and monuments dataset demonstrate the superiority of the proposed approach in terms of style transfer accuracy, content preservation, and visual quality compared to the baseline method, AdaAttN. The CLIP similarity scores further validate the effectiveness of InST in capturing style attributes while maintaining semantic content. The proposed pipeline streamlines the creation of engaging educational content, empowering educators and content creators to produce visually captivating and informative materials efficiently. Show Chinese abstract

Abstract: 本文提出了一种综合的流水线，集成了最先进的技术，以实现高质量的教育图像和视频卡通风格迁移。 所提出的方案结合了基于反转的风格迁移（InST）框架用于图像和视频样式设计，预训练的图像处理变换器（IPT）用于后去噪，以及领域校准翻译网络（DCT-Net）用于更一致的视频风格迁移。 通过微调特定的卡通风格InST，应用IPT进行伪影减少，并利用DCT-Net实现时间一致性，该流水线生成视觉上吸引人且教育有效的样式化内容。 使用风景和纪念碑数据集进行的广泛实验和评估表明，与基准方法AdaAttN相比，所提出的方法在风格迁移准确性、内容保留和视觉质量方面具有优越性。 CLIP相似性分数进一步验证了InST在捕捉样式属性的同时保持语义内容的有效性。 所提出的流水线简化了引人入胜的教育内容的创建，使教育者和内容创作者能够高效地生产视觉上引人注目且信息丰富的材料。