Semantic image editing requires inpainting pixels following a semantic map.
It is a challenging task since this inpainting requires both harmony with the
context and strict compliance with the semantic maps. The majority of the
previous methods proposed for this task try to encode the whole information
from erased images. However, when an object is added to a scene such as a car,
its style cannot be encoded from the context alone. On the other hand, the
models that can output diverse generations struggle to output images that have
seamless boundaries between the generated and unerased parts. Additionally,
previous methods do not have a mechanism to encode the styles of visible and
partially visible objects differently for better performance. In this work, we
propose a framework that can encode visible and partially visible objects with
a novel mechanism to achieve consistency in the style encoding and final
generations. We extensively compare with previous conditional image generation
and semantic image editing algorithms. Our extensive experiments show that our
method significantly improves over the state-of-the-art. Our method not only
achieves better quantitative results but also provides diverse results. Please
refer to the project web page for the released code and demo:
this https URL

本研究提出了一个框架，能够以一种新的机制对可见和部分可见的对象进行编码，以实现样式编码和最终生成之间的一致性。通过与以前的条件图像生成和语义图像编辑算法进行广泛比较，我们的实验表明，我们的方法在技术上显著改进。我们的方法不仅在定量结果上取得了更好的成果，而且提供了多样化的结果。