ARtVista: Gateway To Empower Anyone Into Artist
- URL: http://arxiv.org/abs/2403.08876v1
- Date: Wed, 13 Mar 2024 18:00:57 GMT
- Title: ARtVista: Gateway To Empower Anyone Into Artist
- Authors: Trong-Vu Hoang, Quang-Binh Nguyen, Duy-Nam Ly, Khanh-Duy Le, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le,
- Abstract summary: We propose ARtVista - a novel system integrating AR and generative AI technologies.
ARtVista recommends reference images aligned with users' abstract ideas and generates sketches for users to draw.
We perform a pilot study and reveal positive feedback on its usability.
- Score: 14.700883382465452
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Drawing is an art that enables people to express their imagination and emotions. However, individuals usually face challenges in drawing, especially when translating conceptual ideas into visually coherent representations and bridging the gap between mental visualization and practical execution. In response, we propose ARtVista - a novel system integrating AR and generative AI technologies. ARtVista not only recommends reference images aligned with users' abstract ideas and generates sketches for users to draw but also goes beyond, crafting vibrant paintings in various painting styles. ARtVista also offers users an alternative approach to create striking paintings by simulating the paint-by-number concept on reference images, empowering users to create visually stunning artwork devoid of the necessity for advanced drawing skills. We perform a pilot study and reveal positive feedback on its usability, emphasizing its effectiveness in visualizing user ideas and aiding the painting process to achieve stunning pictures without requiring advanced drawing skills. The source code will be available at https://github.com/htrvu/ARtVista.
Related papers
- Hyperstroke: A Novel High-quality Stroke Representation for Assistive Artistic Drawing [12.71408421022756]
We introduce hyperstroke, a novel stroke representation designed to capture precise fine stroke details.
We propose to model assistive drawing via a transformer-based architecture, to enable intuitive and user-friendly drawing applications.
arXiv Detail & Related papers (2024-08-18T04:05:53Z) - AltCanvas: A Tile-Based Image Editor with Generative AI for Blind or Visually Impaired People [4.41462357579624]
People with visual impairments often struggle to create content that relies heavily on visual elements.
Existing accessible drawing tools, which construct images line by line, are suitable for simple tasks like math but not for more expressive artwork.
Our work integrates generative AI with a constructive approach that provides users with enhanced control and editing capabilities.
arXiv Detail & Related papers (2024-08-05T01:47:36Z) - Impressions: Understanding Visual Semiotics and Aesthetic Impact [66.40617566253404]
We present Impressions, a novel dataset through which to investigate the semiotics of images.
We show that existing multimodal image captioning and conditional generation models struggle to simulate plausible human responses to images.
This dataset significantly improves their ability to model impressions and aesthetic evaluations of images through fine-tuning and few-shot adaptation.
arXiv Detail & Related papers (2023-10-27T04:30:18Z) - SketchDreamer: Interactive Text-Augmented Creative Sketch Ideation [111.2195741547517]
We present a method to generate controlled sketches using a text-conditioned diffusion model trained on pixel representations of images.
Our objective is to empower non-professional users to create sketches and, through a series of optimisation processes, transform a narrative into a storyboard.
arXiv Detail & Related papers (2023-08-27T19:44:44Z) - Interactive Neural Painting [66.9376011879115]
This paper proposes the first approach for Interactive Neural Painting (NP)
We propose I-Paint, a novel method based on a conditional transformer Variational AutoEncoder (VAE) architecture with a two-stage decoder.
Our experiments show that our approach provides good stroke suggestions and compares favorably to the state of the art.
arXiv Detail & Related papers (2023-07-31T07:02:00Z) - Towards Practicality of Sketch-Based Visual Understanding [15.30818342202786]
Sketches have been used to conceptualise and depict visual objects from pre-historic times.
This thesis aims to progress sketch-based visual understanding towards more practicality.
arXiv Detail & Related papers (2022-10-27T03:12:57Z) - A domain adaptive deep learning solution for scanpath prediction of
paintings [66.46953851227454]
This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings.
We introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans.
The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention.
arXiv Detail & Related papers (2022-09-22T22:27:08Z) - Art Creation with Multi-Conditional StyleGANs [81.72047414190482]
A human artist needs a combination of unique skills, understanding, and genuine intention to create artworks that evoke deep feelings and emotions.
We introduce a multi-conditional Generative Adversarial Network (GAN) approach trained on large amounts of human paintings to synthesize realistic-looking paintings that emulate human art.
arXiv Detail & Related papers (2022-02-23T20:45:41Z) - Learning Aesthetic Layouts via Visual Guidance [7.992550355579791]
We explore computational approaches for visual guidance to aid in creating pleasing art and graphic design.
We collected a dataset of art masterpieces and labeled the visual fixations with state-of-art vision models.
We clustered the visual guidance templates of the art masterpieces with unsupervised learning.
We show that the aesthetic visual guidance principles can be learned and integrated into a high-dimensional model and can be queried by the features of graphic elements.
arXiv Detail & Related papers (2021-07-13T17:46:42Z) - DeepFacePencil: Creating Face Images from Freehand Sketches [77.00929179469559]
Existing image-to-image translation methods require a large-scale dataset of paired sketches and images for supervision.
We propose DeepFacePencil, an effective tool that is able to generate photo-realistic face images from hand-drawn sketches.
arXiv Detail & Related papers (2020-08-31T03:35:21Z)
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.