BLT: Bidirectional Layout Transformer for Controllable Layout Generation
- URL: http://arxiv.org/abs/2112.05112v1
- Date: Thu, 9 Dec 2021 18:49:28 GMT
- Title: BLT: Bidirectional Layout Transformer for Controllable Layout Generation
- Authors: Xiang Kong, Lu Jiang, Huiwen Chang, Han Zhang, Yuan Hao, Haifeng Gong,
Irfan Essa
- Abstract summary: We introduce BLT, a bidirectional layout transformer for conditional layout generation.
We verify the proposed model on multiple benchmarks with various fidelity metrics.
Our results demonstrate two key advances to the state-of-the-art layout transformer models.
- Score: 27.239276265955954
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Creating visual layouts is an important step in graphic design. Automatic
generation of such layouts is important as we seek scale-able and diverse
visual designs. Prior works on automatic layout generation focus on
unconditional generation, in which the models generate layouts while neglecting
user needs for specific problems. To advance conditional layout generation, we
introduce BLT, a bidirectional layout transformer. BLT differs from
autoregressive decoding as it first generates a draft layout that satisfies the
user inputs and then refines the layout iteratively. We verify the proposed
model on multiple benchmarks with various fidelity metrics. Our results
demonstrate two key advances to the state-of-the-art layout transformer models.
First, our model empowers layout transformers to fulfill controllable layout
generation. Second, our model slashes the linear inference time in
autoregressive decoding into a constant complexity, thereby achieving 4x-10x
speedups in generating a layout at inference time.
Related papers
- PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM [58.67882997399021]
Our research introduces a unified framework for automated graphic layout generation.
Our data-driven method employs structured text (JSON format) and visual instruction tuning to generate layouts.
We conduct extensive experiments and achieved state-of-the-art (SOTA) performance on public multi-modal layout generation benchmarks.
arXiv Detail & Related papers (2024-06-05T03:05:52Z) - Desigen: A Pipeline for Controllable Design Template Generation [69.51563467689795]
Desigen is an automatic template creation pipeline which generates background images as well as layout elements over the background.
We propose two techniques to constrain the saliency distribution and reduce the attention weight in desired regions during the background generation process.
Experiments demonstrate that the proposed pipeline generates high-quality templates comparable to human designers.
arXiv Detail & Related papers (2024-03-14T04:32:28Z) - Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints [53.66698106829144]
We propose a unified model to handle a broad range of layout generation tasks.
The model is based on continuous diffusion models.
Experiment results show that LACE produces high-quality layouts.
arXiv Detail & Related papers (2024-02-07T11:12:41Z) - Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation [30.101562738257588]
Content-aware graphic layout generation aims to automatically arrange visual elements along with a given content, such as an e-commerce product image.
We show that a simple retrieval augmentation can significantly improve the generation quality.
Our model, which is named Retrieval-Augmented Layout Transformer (RALF), retrieves nearest neighbor layout examples based on an input image and feeds these results into an autoregressive generator.
arXiv Detail & Related papers (2023-11-22T18:59:53Z) - Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation [147.81509219686419]
We propose a diagnostic benchmark for layout-guided image generation that examines four categories of spatial control skills: number, position, size, and shape.
Next, we propose IterInpaint, a new baseline that generates foreground and background regions step-by-step via inpainting.
We show comprehensive ablation studies on IterInpaint, including training task ratio, crop&paste vs. repaint, and generation order.
arXiv Detail & Related papers (2023-04-13T16:58:33Z) - LayoutDiffusion: Improving Graphic Layout Generation by Discrete
Diffusion Probabilistic Models [50.73105631853759]
We present a novel generative model named LayoutDiffusion for automatic layout generation.
It learns to reverse a mild forward process, in which layouts become increasingly chaotic with the growth of forward steps.
It enables two conditional layout generation tasks in a plug-and-play manner without re-training and achieves better performance than existing methods.
arXiv Detail & Related papers (2023-03-21T04:41:02Z) - Diverse Multimedia Layout Generation with Multi Choice Learning [27.542940346258916]
In contrast to standard prediction tasks, there are a range of acceptable layouts which depend on user preferences.
Existing machine learning models treat layouts as a single choice prediction problem.
We present an auto-regressive neural network architecture, called LayoutMCL, that uses multi-choice prediction and winner-takes-all loss.
arXiv Detail & Related papers (2023-01-16T22:53:55Z) - LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer [80.61492265221817]
Graphic layout designs play an essential role in visual communication.
Yet handcrafting layout designs is skill-demanding, time-consuming, and non-scalable to batch production.
Generative models emerge to make design automation scalable but it remains non-trivial to produce designs that comply with designers' desires.
arXiv Detail & Related papers (2022-12-19T21:57:35Z) - Constrained Graphic Layout Generation via Latent Optimization [17.05026043385661]
We generate graphic layouts that can flexibly incorporate design semantics, either specified implicitly or explicitly by a user.
Our approach builds on a generative layout model based on a Transformer architecture, and formulates the layout generation as a constrained optimization problem.
We show in the experiments that our approach is capable of generating realistic layouts in both constrained and unconstrained generation tasks with a single model.
arXiv Detail & Related papers (2021-08-02T13:04:11Z) - Generative Layout Modeling using Constraint Graphs [37.78500605563527]
We propose a new generative model for layout generation.
First, we generate the layout elements as nodes in a layout graph.
Second, we compute constraints between layout elements as edges in the layout graph.
Third, we solve for the final layout using constrained optimization.
arXiv Detail & Related papers (2020-11-26T18:18:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.