Diverse Semantic Image Editing with Style Codes
Hakan Sivuk Aysegul Dundar
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:
https://github.com/hakansivuk/DivSem.