Image-to-image (I2I) translation is a challenging topic in computer vision. We divide this problem into three tasks: strongly constrained translation, normally constrained translation, and weakly constrained translation. The constraint here indicates the extent to which the content or semantic information in the original image is preserved. Although previous approaches have achieved good performance in weakly constrained tasks, they failed to fully preserve the content in both strongly and normally constrained tasks, including photo-realism synthesis, style transfer, and colorization, etc. To achieve content-preserving transfer in strongly constrained and normally constrained tasks, we propose StyleFlow, a new I2I translation model that consists of normalizing flows and a novel Style-Aware Normalization (SAN) module. With the invertible network structure, StyleFlow first projects input images into deep feature space in the forward pass, while the backward pass utilizes the SAN module to perform content-fixed feature transformation and then projects back to image space. Our model supports both image-guided translation and multi-modal synthesis. We evaluate our model in several I2I translation benchmarks, and the results show that the proposed model has advantages over previous methods in both strongly constrained and normally constrained tasks.
翻译:图像到映像( I2I) 翻译是计算机视觉中具有挑战性的话题。 我们将这一问题分为三个任务: 严格限制翻译、 通常受限制的翻译和受限制的翻译。 此处的限制表明原始图像中的内容或语义信息被保存的程度。 虽然以前的方法在受限制的工作中都取得了良好的表现, 但是它们未能在强烈和通常受限制的任务中充分保存内容, 包括照片- 现实合成、 风格传输和色彩化等。 为了在受严格限制和通常受限制的任务中实现内容保留传输, 我们提议了Styleflow, 一个新的I2I2I翻译模型, 由正常化流程和新颖的Style- Awardalnical化( SAN) 模块组成。 由于网络结构不可改变, StyleFlow 最初的项目将图像输入前过路深处的地貌空间, 而后传则利用 SAN 模块进行内容固定的特征转换, 然后又将项目返回图像空间。 为了在受到强烈的翻译和多式合成的合成合成, 我们用一些I2I2I 翻译基准来评估我们的模型, 并且结果通常会限制。