8 papers with code • 6 benchmarks • 3 datasets
Layout-to-image generation its the task to generate a scene based on the given layout. The layout describes the location of the objects to be included in the output image. In this section, you can find state-of-the-art leaderboards for Layout-to-image generation.
In our work, we address the novel problem of image manipulation from scene graphs, in which a user can edit images by merely applying changes in the nodes or edges of a semantic graph that is generated from the image.
This paper focuses on a recent emerged task, layout-to-image, to learn generative models that are capable of synthesizing photo-realistic images from spatial layout (i. e., object bounding boxes configured in an image lattice) and style (i. e., structural and appearance variations encoded by latent vectors).
Ranked #1 on Layout-to-Image Generation on COCO-Stuff 128x128
Despite remarkable recent progress on both unconditional and conditional image synthesis, it remains a long-standing problem to learn generative models that are capable of synthesizing realistic and sharp images from reconfigurable spatial layout (i. e., bounding boxes + class labels in an image lattice) and style (i. e., structural and appearance variations encoded by latent vectors), especially at high resolution.
Ranked #2 on Layout-to-Image Generation on COCO-Stuff 64x64
Generating realistic images of complex visual scenes becomes challenging when one wishes to control the structure of the generated images.
Ranked #1 on Layout-to-Image Generation on Visual Genome 256x256
We argue that these are caused by the lack of context-aware object and stuff feature encoding in their generators, and location-sensitive appearance representation in their discriminators.
In particular, layout-to-image generation models have gained significant attention due to their capability to generate realistic complex images containing distinct objects.