Layout-to-Image Generation
18 papers with code • 7 benchmarks • 4 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.
Benchmarks
These leaderboards are used to track progress in Layout-to-Image Generation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
MS COCO
Visual Genome
COCO-Stuff
LayoutBench
Most implemented papers
High-Resolution Image Synthesis with Latent Diffusion Models
compvis/stable-diffusion
•
•
CVPR 2022
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond.
Image Generation from Scene Graphs
To overcome this limitation we propose a method for generating images from scene graphs, enabling explicitly reasoning about objects and their relationships.
Image Synthesis From Reconfigurable Layout and Style
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.
Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis
iVMCL/LostGANs
•
•
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).
Specifying Object Attributes and Relations in Interactive Scene Generation
ashual/scene_generation
•
•
ICCV 2019
We introduce a method for the generation of images from an input scene graph.
Learning Canonical Representations for Scene Graph to Image Generation
roeiherz/CanonicalSg2Im
•
•
ECCV 2020
Generating realistic images of complex visual scenes becomes challenging when one wishes to control the structure of the generated images.
LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation
dcdcvgroup/layout-diffusion-mindspore
•
•
CVPR 2023
To overcome the difficult multimodal fusion of image and layout, we propose to construct a structural image patch with region information and transform the patched image into a special layout to fuse with the normal layout in a unified form.
Semantic Image Manipulation Using Scene Graphs
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.
Generating Annotated High-Fidelity Images Containing Multiple Coherent Objects
Cynetics/MSGNet
•
•
In particular, layout-to-image generation models have gained significant attention due to their capability to generate realistic complex images containing distinct objects.
Context-Aware Layout to Image Generation with Enhanced Object Appearance
wtliao/layout2img
•
•
CVPR 2021
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.
