Design-o-meter: Towards Evaluating and Refining Graphic Designs
- URL: http://arxiv.org/abs/2411.14959v1
- Date: Fri, 22 Nov 2024 14:17:46 GMT
- Title: Design-o-meter: Towards Evaluating and Refining Graphic Designs
- Authors: Sahil Goyal, Abhinav Mahajan, Swasti Mishra, Prateksha Udhayanan, Tripti Shukla, K J Joseph, Balaji Vasan Srinivasan,
- Abstract summary: We introduce Design-o-meter, a data-driven methodology to quantify the goodness of graphic designs.
To the best of our knowledge, Design-o-meter is the first approach that scores and refines designs in a unified framework.
- Score: 11.416650723712968
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graphic designs are an effective medium for visual communication. They range from greeting cards to corporate flyers and beyond. Off-late, machine learning techniques are able to generate such designs, which accelerates the rate of content production. An automated way of evaluating their quality becomes critical. Towards this end, we introduce Design-o-meter, a data-driven methodology to quantify the goodness of graphic designs. Further, our approach can suggest modifications to these designs to improve its visual appeal. To the best of our knowledge, Design-o-meter is the first approach that scores and refines designs in a unified framework despite the inherent subjectivity and ambiguity of the setting. Our exhaustive quantitative and qualitative analysis of our approach against baselines adapted for the task (including recent Multimodal LLM-based approaches) brings out the efficacy of our methodology. We hope our work will usher more interest in this important and pragmatic problem setting.
Related papers
- Multimodal graph representation learning for website generation based on visual sketch [1.515687944002438]
Design2Code problem involves converting digital designs into functional source code.
Traditional approaches often struggle with accurately interpreting the intricate visual details and structural relationships inherent in webpage designs.
We propose a novel method that leverages multimodal graph representation learning to address these challenges.
arXiv Detail & Related papers (2025-04-25T22:48:10Z) - Design Editing for Offline Model-based Optimization [18.701760631151316]
offline model-based optimization (MBO) aims to maximize a black-box objective function using only an offline dataset of designs and scores.
A common approach involves training a surrogate model using existing designs and their corresponding scores, and then generating new designs through gradient-based updates with respect to the surrogate model.
This method suffers from the out-of-distribution issue, where the surrogate model may erroneously predict high scores for unseen designs.
We introduce a novel method, Design Editing for Offline Model-based Optimization (DEMO), which leverages a diffusion prior to calibrate overly optimized designs.
arXiv Detail & Related papers (2024-05-22T20:00:19Z) - Improving Subject-Driven Image Synthesis with Subject-Agnostic Guidance [62.15866177242207]
We show that through constructing a subject-agnostic condition, one could obtain outputs consistent with both the given subject and input text prompts.
Our approach is conceptually simple and requires only minimal code modifications, but leads to substantial quality improvements.
arXiv Detail & Related papers (2024-05-02T15:03:41Z) - Evaluation Metrics for Automated Typographic Poster Generation [0.24578723416255752]
We propose a set of metrics for typographic design evaluation, focusing on their legibility.
We also integrate emotion recognition to identify text semantics automatically and analyse the performance of the approach.
arXiv Detail & Related papers (2024-02-10T13:18:10Z) - Design Space Exploration and Explanation via Conditional Variational
Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE)
The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
arXiv Detail & Related papers (2022-11-29T17:28:31Z) - Material Prediction for Design Automation Using Graph Representation
Learning [5.181429907321226]
We introduce a graph representation learning framework that supports the material prediction of bodies in assemblies.
We formulate the material selection task as a node-level prediction task over the assembly graph representation of CAD models and tackle it using Graph Neural Networks (GNNs)
The proposed framework can scale to large datasets and incorporate designers' knowledge into the learning process.
arXiv Detail & Related papers (2022-09-26T15:49:35Z) - Investigating Positive and Negative Qualities of Human-in-the-Loop
Optimization for Designing Interaction Techniques [55.492211642128446]
Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives.
Model-based computational design algorithms assist designers by generating design examples during design.
Black box methods for assistance, on the other hand, can work with any design problem.
arXiv Detail & Related papers (2022-04-15T20:40:43Z) - Information-Theoretic Odometry Learning [83.36195426897768]
We propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation.
The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language.
arXiv Detail & Related papers (2022-03-11T02:37:35Z) - Unadversarial Examples: Designing Objects for Robust Vision [100.4627585672469]
We develop a framework that exploits the sensitivity of modern machine learning algorithms to input perturbations in order to design "robust objects"
We demonstrate the efficacy of the framework on a wide variety of vision-based tasks ranging from standard benchmarks to (in-simulation) robotics.
arXiv Detail & Related papers (2020-12-22T18:26:07Z) - Towards Fine-grained Human Pose Transfer with Detail Replenishing
Network [96.54367984986898]
Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality.
Existing HPT methods often suffer from three fundamental issues: detail deficiency, content ambiguity and style inconsistency.
We develop a more challenging yet practical HPT setting, termed as Fine-grained Human Pose Transfer (FHPT), with a higher focus on semantic fidelity and detail replenishment.
arXiv Detail & Related papers (2020-05-26T03:05:23Z)
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.