论文标题
Blendgan：通过空间图像身份调节学习和融合单个图像的内部分布
BlendGAN: Learning and Blending the Internal Distributions of Single Images by Spatial Image-Identity Conditioning
论文作者
论文摘要
近年来，对单个图像进行培训的生成模型引起了极大的关注。单个图像生成方法旨在学习多个尺度上单个自然图像的内部贴剂分布。这些模型可用于绘制类似于训练图像的不同样本，以及解决涉及该特定图像的许多图像编辑和恢复任务。在这里，我们介绍了一个扩展框架，该框架可以通过使用具有空间变化的图像识别条件的单个模型同时学习多个图像的内部分布。我们的布伦根（Blendgan）为单位模型不支持的应用打开了大门，包括两个或更多任意图像之间的变形，融合和结构质量融合。
Training a generative model on a single image has drawn significant attention in recent years. Single image generative methods are designed to learn the internal patch distribution of a single natural image at multiple scales. These models can be used for drawing diverse samples that semantically resemble the training image, as well as for solving many image editing and restoration tasks that involve that particular image. Here, we introduce an extended framework, which allows to simultaneously learn the internal distributions of several images, by using a single model with spatially varying image-identity conditioning. Our BlendGAN opens the door to applications that are not supported by single-image models, including morphing, melding, and structure-texture fusion between two or more arbitrary images.
