论文标题
条件图像合成的嵌套比例编辑
Nested Scale Editing for Conditional Image Synthesis
论文作者
论文摘要
我们提出了一种图像合成方法，该方法在潜在代码空间中提供了分层导航。借助少量的部分或非常低分辨率的图像，我们的方法可以始终如一地表现出最直接的对应图像，即产生最接近的采样图像与地面真相。我们通过与规模无关的编辑实现这一目标，同时扩大了规模特定的多样性。缩放量表的分离损失可以实现尺度独立的依赖性。特定于比例的多样性是通过纳入进行性多元化约束来创建的。我们通过共享共同的潜在代码在范围内引入语义持久性。它们共同提供了对图像合成过程的更好控制。我们通过各种任务评估了我们提出的方法的有效性，包括图像支出，图像序列和跨域图像翻译。
We propose an image synthesis approach that provides stratified navigation in the latent code space. With a tiny amount of partial or very low-resolution image, our approach can consistently out-perform state-of-the-art counterparts in terms of generating the closest sampled image to the ground truth. We achieve this through scale-independent editing while expanding scale-specific diversity. Scale-independence is achieved with a nested scale disentanglement loss. Scale-specific diversity is created by incorporating a progressive diversification constraint. We introduce semantic persistency across the scales by sharing common latent codes. Together they provide better control of the image synthesis process. We evaluate the effectiveness of our proposed approach through various tasks, including image outpainting, image superresolution, and cross-domain image translation.
