Abstract
Video color style transfer aims to transform the color style of an original video by using a reference style image. Most existing methods employ neural networks, which come with challenges like opaque transfer processes and limited user control over the outcomes. Typically, users cannot fine-tune the resulting images or videos. To tackle this issue, we introduce a method that predicts specific parameters for color style transfer using two images. Initially, we train a neural network to learn the corresponding color adjustment parameters. When applying style transfer to a video, we fine-tune the network with key frames from the video and the chosen style image, generating precise transformation parameters. These are then applied to convert the color style of both images and videos. Our experimental results demonstrate that our algorithm surpasses current methods in color style transfer quality. Moreover, each parameter in our method has a specific, interpretable meaning, enabling users to understand the color style transfer process and allowing them to perform manual fine-tuning if desired.
Abstract (translated)
视频色彩风格迁移旨在通过使用参考风格图像来转换原始视频的色彩风格。现有的大多数方法都采用了神经网络，这带来了诸如转移过程不透明和用户对结果控制有限等挑战。通常情况下，用户无法调整最终生成的图像或视频。为了解决这个问题，我们提出了一种利用两张图片预测特定色彩风格迁移参数的方法。首先，我们训练一个神经网络来学习相应的颜色调整参数。在将风格迁移应用于视频时，我们会使用视频的关键帧和选定的风格图像对网络进行微调，从而生成精确的转换参数。这些参数随后被用于转换图像和视频的颜色风格。实验结果表明，我们的算法在色彩风格迁移的质量上超过了当前的方法。此外，我们方法中的每个参数都有具体的、可解释的意义，使得用户能够理解色彩风格迁移的过程，并允许他们根据需要进行手动微调。
URL
https://arxiv.org/abs/2411.00335
