Designer Modeling through Design Style Clustering
- URL: http://arxiv.org/abs/2004.01697v3
- Date: Thu, 20 Jan 2022 14:08:47 GMT
- Title: Designer Modeling through Design Style Clustering
- Authors: Alberto Alvarez, Jose Font, Julian Togelius
- Abstract summary: We propose modeling designer style in mixed-initiative game content creation tools as archetypical design traces.
This method is implemented in the Evolutionary Dungeon Designer, a research platform for mixed-initiative systems to create adventure and dungeon crawler games.
- Score: 4.4447051343759965
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose modeling designer style in mixed-initiative game content creation
tools as archetypical design traces. These design traces are formulated as
transitions between design styles; these design styles are in turn found
through clustering all intermediate designs along the way to making a complete
design. This method is implemented in the Evolutionary Dungeon Designer, a
research platform for mixed-initiative systems to create adventure and dungeon
crawler games. We present results both in the form of design styles for rooms,
which can be analyzed to better understand the kind of rooms designed by users,
and in the form of archetypical sequences between these rooms, i.e., Designer
Personas.
Related papers
- Inkspire: Supporting Design Exploration with Generative AI through Analogical Sketching [16.33879333386818]
Inkspire is a sketch-driven tool that supports designers in prototyping product design concepts.
In a study comparing Inkspire to ControlNet, we found that Inkspire supported designers with more inspiration and exploration of design ideas.
arXiv Detail & Related papers (2025-01-30T18:59:04Z) - Automatic Layout Planning for Visually-Rich Documents with Instruction-Following Models [81.6240188672294]
In graphic design, non-professional users often struggle to create visually appealing layouts due to limited skills and resources.
We introduce a novel multimodal instruction-following framework for layout planning, allowing users to easily arrange visual elements into tailored layouts.
Our method not only simplifies the design process for non-professionals but also surpasses the performance of few-shot GPT-4V models, with mIoU higher by 12% on Crello.
arXiv Detail & Related papers (2024-04-23T17:58:33Z) - I-Design: Personalized LLM Interior Designer [57.00412237555167]
I-Design is a personalized interior designer that allows users to generate and visualize their design goals through natural language communication.
I-Design starts with a team of large language model agents that engage in dialogues and logical reasoning with one another.
The final design is then constructed in 3D by retrieving and integrating assets from an existing object database.
arXiv Detail & Related papers (2024-04-03T16:17:53Z) - Jigsaw: Supporting Designers to Prototype Multimodal Applications by Chaining AI Foundation Models [4.435190193476497]
Jigsaw is a prototype system that employs puzzle pieces as metaphors to represent foundation models.
Designers can combine different foundation model capabilities across various modalities by assembling compatible puzzle pieces.
arXiv Detail & Related papers (2023-10-12T17:57:57Z) - PosterLayout: A New Benchmark and Approach for Content-aware
Visual-Textual Presentation Layout [62.12447593298437]
Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements.
We propose design sequence formation (DSF) that reorganizes elements in layouts to imitate the design processes of human designers.
A novel CNN-LSTM-based conditional generative adversarial network (GAN) is presented to generate proper layouts.
arXiv Detail & Related papers (2023-03-28T12:48:36Z) - 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) - Threshold Designer Adaptation: Improved Adaptation for Designers in
Co-creative Systems [0.9645196221785693]
We present threshold designer adaptation: a novel method for adapting a creative ML model to an individual designer.
We find that designers prefer our proposed method and produce higher quality content in comparison to an existing baseline.
arXiv Detail & Related papers (2022-05-19T01:13:22Z) - 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) - StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval [119.03470556503942]
Crossmodal matching problem is typically solved by learning a joint embedding space where semantic content shared between photo and sketch modalities are preserved.
An effective model needs to explicitly account for this style diversity, crucially, to unseen user styles.
Our model can not only disentangle the cross-modal shared semantic content, but can adapt the disentanglement to any unseen user style as well, making the model truly agnostic.
arXiv Detail & Related papers (2021-03-29T15:44:19Z) - AI Assisted Apparel Design [2.20200533591633]
We propose two design generation assistants namely Apparel-Style-Merge and Apparel-Style-Transfer.
Apparel-Style-Merge generates new designs by combining high level components of apparels.
Apparel-Style-Transfer generates multiple customization of apparels by applying different styles, colors and patterns.
arXiv Detail & Related papers (2020-07-09T17:24:40Z) - Evaluating Mixed-Initiative Procedural Level Design Tools using a
Triple-Blind Mixed-Method User Study [0.0]
A tool which generates levels using interactive evolutionary optimisation was designed for this study.
The tool identifies level design patterns in an initial hand-designed map and uses that information to drive an interactive optimisation algorithm.
A rigorous user study was designed which compared the experiences of designers using the mixed-initiative tool to designers who were given a tool which provided completely random level suggestions.
arXiv Detail & Related papers (2020-05-15T11:40:53Z)
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.