用可逆流模型实现高效精准的图像颜色迁移。
Color Transfer with Modulated Flows
- 基于最优传输构建可逆颜色变换，引入中间统一色分布
- 训练后无需微调即可对新图像对生成迁移方案，支持4K处理
- 输出仅含色彩风格的嵌入向量，适合风格迁移与图像编辑
本文提出基于修正流的调制流（ModFlows）方法，用于图像间颜色迁移。目标是将目标图像的颜色调整为匹配参考图像的颜色分布。该方法基于最优传输，在RGB空间中执行可逆变换。利用流的双射性，引入共同中间颜色分布并构建修正流数据集。通过在该数据集上训练编码器，可预测新图像的修正模型权重。在一组最优传输计划上训练后，该方法能直接生成新分布对的传输方案，无需额外微调。此外，训练后的编码器可生成仅反映颜色风格的图像嵌入。所提方法支持4K图像处理，在内容和风格相似性上达到当前最优性能。代码已开源。
原文摘要 · Abstract (English)
In this work, we introduce Modulated Flows (ModFlows), a novel approach for color transfer between images based on rectified flows. The primary goal of the color transfer is to adjust the colors of a target image to match the color distribution of a reference image. Our technique is based on optimal transport and executes color transfer as an invertible transformation within the RGB color space. The ModFlows utilizes the bijective property of flows, enabling us to introduce a common intermediate color distribution and build a dataset of rectified flows. We train an encoder on this dataset to predict the weights of a rectified model for new images. After training on a set of optimal transport plans, our approach can generate plans for new pairs of distributions without additional fine-tuning. We additionally show that the trained encoder provides an image embedding, associated only with its color style. The presented method is capable of processing 4K images and achieves the state-of-the-art performance in terms of content and style similarity. Our source code is available at https://github.com/maria-larchenko/modflows
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。