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image-generation

Layout-to-Image Generation

3260 papers • 126 benchmarks • 313 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.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in image-generation

Trend

Dataset

Best Model

Actions

COCO-Stuff 64x64

COCO-Stuff 64x64

COCO-Stuff 128x128

COCO-Stuff 128x128

Visual Genome 128x128

Libraries

i

Use these libraries to find image-generation models and implementations

Datasets

No datasets available.

Subtasks

No subtasks available.

Most implemented papers

High-Resolution Image Synthesis with Latent Diffusion Models

A. Blattmann, Robin Rombach, Dominik Lorenz, B. Ommer, Patrick Esser•Sun Dec 19 2021

These latent diffusion models achieve new state of the art scores for image inpainting and class-conditional image synthesis and highly competitive performance on various tasks, including unconditional image generation, text-to-image synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.

21712

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Visual Genome 128x128

COCO-Stuff 256x256

COCO-Stuff 256x256

Visual Genome 64x64

Visual Genome 64x64

Visual Genome 256x256

Visual Genome 256x256

LayoutBench

LayoutBench

0

Image Generation from Scene Graphs

Justin Johnson, Li Fei-Fei, Agrim Gupta•Tue Apr 03 2018

This work proposes a method for generating images from scene graphs, enabling explicitly reasoning about objects and their relationships, and validates this approach on Visual Genome and COCO-Stuff.

887 0

Image Synthesis From Reconfigurable Layout and Style

Tianfu Wu•Mon Aug 19 2019

This paper presents a layout- and style-based architecture for generative adversarial networks (termed LostGANs) that can be trained end-to-end to generate images from reconfigurable layout and style.

155 0

Specifying Object Attributes and Relations in Interactive Scene Generation

Lior Wolf, Oron Ashual•Tue Sep 10 2019

The method separates between a layout embedding and an appearance embedding, which leads to generated images that better match the scene graph, have higher visual quality, and support more complex scene graphs.

188 0

Learning Canonical Representations for Scene Graph to Image Generation

Gal Chechik, Trevor Darrell, Roei Herzig, Amir Bar, A. Globerson, Huijuan Xu•Sun Dec 15 2019

This work presents a novel model that addresses semantic equivalence issues in graphs by learning canonical graph representations from the data, resulting in improved image generation for complex visual scenes.

118 0

LayoutDiffusion: Controllable Diffusion Model for Layout-to-Image Generation

Zhongang Qi, Guangcong Zheng, Xianpan Zhou, Xuewei Li, Ying Shan, Xi Li•Wed Mar 29 2023

A diffusion model named LayoutDiffusion is proposed that can obtain higher generation quality and greater controllability than the previous works and is proposed 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.

293 0

Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis

Tianfu Wu•Tue Mar 24 2020

An intuitive paradigm for the task, layout-to-mask- to-image, which learns to unfold object masks in a weakly-supervised way based on an input layout and object style codes is proposed and a method built on Generative Adversarial Networks (GANs) is presented.

92 0

Context-Aware Layout to Image Generation with Enhanced Object Appearance

T. Xiang, Yongxin Yang, M. Yang, B. Rosenhahn, Yi-Zhe Song, Wentong Liao, Sen He•Sun Mar 21 2021

A context-aware feature transformation module is introduced in the generator to ensure that the generated feature encoding of either object or stuff is aware of other coexisting objects/stuff in the scene.

60 0

Generating Annotated High-Fidelity Images Containing Multiple Coherent Objects

Bryan G. Cardenas, Devanshu Arya, D. Gupta•Sun Jun 21 2020

This work proposes a multi-object generation framework that can synthesize images with multiple objects without explicitly requiring their contextual information during the generation process and shows that augmenting the training set with the samples generated by the approach improves the performance of existing models.

7 0

Semantic Image Manipulation Using Scene Graphs

N. Navab, C. Rupprecht, Gregory Hager, Federico Tombari, Helisa Dhamo, Iro Laina, Azade Farshad•Mon Apr 06 2020

This work introduces a spatio-semantic scene graph network that does not require direct supervision for constellation changes or image edits, and makes it possible to train the system from existing real-world datasets with no additional annotation effort.

136 0

Adding a benchmark result helps the community track progress.

Layout-to-Image Generation | State-of-the-Art