Influencer: Empowering Everyday Users in Creating Promotional Posts via AI-infused Exploration and Customization
- URL: http://arxiv.org/abs/2407.14928v1
- Date: Sat, 20 Jul 2024 16:27:49 GMT
- Title: Influencer: Empowering Everyday Users in Creating Promotional Posts via AI-infused Exploration and Customization
- Authors: Xuye Liu, Annie Sun, Pengcheng An, Tengfei Ma, Jian Zhao,
- Abstract summary: Influen is an interactive tool to assist novice creators in crafting high-quality promotional post designs.
Within Influencer, we contribute a multi-dimensional recommendation framework that allows users to intuitively generate new ideas.
Influential implements a holistic promotional post design system that supports context-aware image and caption exploration.
- Score: 11.9449656506593
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Creating promotional posts on social platforms enables everyday users to disseminate their creative outcomes, engage in community exchanges, or generate additional income from micro-businesses. However, creating eye-catching posts combining both original, appealing images and articulate, effective captions can be rather challenging and time-consuming for everyday users who are mostly design novices. We propose Influen, an interactive tool to assist novice creators in crafting high-quality promotional post designs, achieving quick design ideation and unencumbered content creation through AI. Within Influencer, we contribute a multi-dimensional recommendation framework that allows users to intuitively generate new ideas through example-based image and caption recommendation. Further, Influencer implements a holistic promotional post design system that supports context-aware image and caption exploration considering brand messages and user-specified design constraints, flexible fusion of various images and captions, and a mind-map-like layout for thinking tracking and post-recording. We evaluated Influencer with 12 design enthusiasts through an in-lab user study by comparing it to a baseline combining Google Search + Figma. Quantitative and qualitative results demonstrate that \sysname{} is effective in assisting design novices to generate ideas as well as creative and diverse promotional posts with user-friendly interaction.
Related papers
- MetaDesigner: Advancing Artistic Typography through AI-Driven, User-Centric, and Multilingual WordArt Synthesis [65.78359025027457]
MetaDesigner revolutionizes artistic typography by leveraging the strengths of Large Language Models (LLMs) to drive a design paradigm centered around user engagement.
A comprehensive feedback mechanism harnesses insights from multimodal models and user evaluations to refine and enhance the design process iteratively.
Empirical validations highlight MetaDesigner's capability to effectively serve diverse WordArt applications, consistently producing aesthetically appealing and context-sensitive results.
arXiv Detail & Related papers (2024-06-28T11:58:26Z) - Empowering Visual Creativity: A Vision-Language Assistant to Image Editing Recommendations [109.65267337037842]
We introduce the task of Image Editing Recommendation (IER)
IER aims to automatically generate diverse creative editing instructions from an input image and a simple prompt representing the users' under-specified editing purpose.
We introduce Creativity-Vision Language Assistant(Creativity-VLA), a multimodal framework designed specifically for edit-instruction generation.
arXiv Detail & Related papers (2024-05-31T18:22:29Z) - Visual Concept-driven Image Generation with Text-to-Image Diffusion Model [65.96212844602866]
Text-to-image (TTI) models have demonstrated impressive results in generating high-resolution images of complex scenes.
Recent approaches have extended these methods with personalization techniques that allow them to integrate user-illustrated concepts.
However, the ability to generate images with multiple interacting concepts, such as human subjects, as well as concepts that may be entangled in one, or across multiple, image illustrations remains illusive.
We propose a concept-driven TTI personalization framework that addresses these core challenges.
arXiv Detail & Related papers (2024-02-18T07:28:37Z) - Social Reward: Evaluating and Enhancing Generative AI through
Million-User Feedback from an Online Creative Community [63.949893724058846]
Social reward as a form of community recognition provides a strong source of motivation for users of online platforms to engage and contribute with content.
This work pioneers a paradigm shift, unveiling Social Reward - an innovative reward modeling framework.
We embark on an extensive journey of dataset curation and refinement, drawing from Picsart: an online visual creation and editing platform.
arXiv Detail & Related papers (2024-02-15T10:56:31Z) - The role of interface design on prompt-mediated creativity in Generative
AI [0.0]
We analyze more than 145,000 prompts from two Generative AI platforms.
We find that users exhibit a tendency towards exploration of new topics over exploitation of concepts visited previously.
arXiv Detail & Related papers (2023-11-30T22:33:34Z) - 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) - Can you recommend content to creatives instead of final consumers? A
RecSys based on user's preferred visual styles [69.69160476215895]
This report is an extension of the paper "Learning Users' Preferred Visual Styles in an Image Marketplace", presented at ACM RecSys '22.
We design a RecSys that learns visual styles preferences to the semantics of the projects users work on.
arXiv Detail & Related papers (2022-08-23T12:11:28Z) - Gaud\'i: Conversational Interactions with Deep Representations to
Generate Image Collections [14.012745542766506]
Gaud'i was developed to help designers search for inspirational images using natural language.
Ours is the first attempt to represent mood-boards as the stories that designers tell when presenting a creative direction to a client.
arXiv Detail & Related papers (2021-12-05T07:02:33Z) - Scaling Creative Inspiration with Fine-Grained Functional Facets of
Product Ideas [21.62996957134357]
Web-scale repositories of products, patents and scientific papers offer an opportunity for creating automated systems.
Yet the common representation of ideas is in the form of raw textual descriptions.
We propose a novel computational representation that automatically breaks up products into fine-grained functional facets.
arXiv Detail & Related papers (2021-02-19T06:30:41Z)
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.