AI Assisted Apparel Design

• URL: http://arxiv.org/abs/2007.04950v2
• Date: Fri, 10 Jul 2020 17:14:17 GMT
• Title: AI Assisted Apparel Design
• Authors: Alpana Dubey, Nitish Bhardwaj, Kumar Abhinav, Suma Mani Kuriakose, Sakshi Jain and Veenu Arora
• Abstract summary: 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.
• Score: 2.20200533591633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fashion is a fast-changing industry where designs are refreshed at large scale every season. Moreover, it faces huge challenge of unsold inventory as not all designs appeal to customers. This puts designers under significant pressure. Firstly, they need to create innumerous fresh designs. Secondly, they need to create designs that appeal to customers. Although we see advancements in approaches to help designers analyzing consumers, often such insights are too many. Creating all possible designs with those insights is time consuming. In this paper, we propose a system of AI assistants that assists designers in their design journey. The proposed system assists designers in analyzing different selling/trending attributes of apparels. 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 whereas Apparel-Style-Transfer generates multiple customization of apparels by applying different styles, colors and patterns. We compose a new dataset, named DeepAttributeStyle, with fine-grained annotation of landmarks of different apparel components such as neck, sleeve etc. The proposed system is evaluated on a user group consisting of people with and without design background. Our evaluation result demonstrates that our approach generates high quality designs that can be easily used in fabrication. Moreover, the suggested designs aid to the designers creativity.

Related papers

• AIpparel: A Large Multimodal Generative Model for Digital Garments [71.12933771326279]
  We introduce AIpparel, a large multimodal model for generating and editing sewing patterns. Our model fine-tunes state-of-the-art large multimodal models on a custom-curated large-scale dataset of over 120,000 unique garments. We propose a novel tokenization scheme that concisely encodes these complex sewing patterns so that LLMs can learn to predict them efficiently.
  arXiv  Detail & Related papers  (2024-12-05T07:35:19Z)
• 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)
• HAIFIT: Human-to-AI Fashion Image Translation [6.034505799418777]
  We introduce HAIFIT, a novel approach that transforms sketches into high-fidelity, lifelike clothing images. Our method excels in preserving the distinctive style and intricate details essential for fashion design applications.
  arXiv  Detail & Related papers  (2024-03-13T16:06:07Z)
• DressCode: Autoregressively Sewing and Generating Garments from Text Guidance [61.48120090970027]
  DressCode aims to democratize design for novices and offer immense potential in fashion design, virtual try-on, and digital human creation. We first introduce SewingGPT, a GPT-based architecture integrating cross-attention with text-conditioned embedding to generate sewing patterns. We then tailor a pre-trained Stable Diffusion to generate tile-based Physically-based Rendering (PBR) textures for the garments.
  arXiv  Detail & Related papers  (2024-01-29T16:24:21Z)
• Infographics Wizard: Flexible Infographics Authoring and Design Exploration [48.93421725740813]
  Infographics are an aesthetic visual representation of information following specific design principles of human perception. We propose a semi-automated infographic framework for general structured and flow-based infographic design generation. For novice designers, our framework automatically creates and ranks infographic designs for a user-provided text with no requirement for design input. We will also contribute an individual visual group (VG) designs dataset (in SVG) along with a 1k complete infographic image dataset with segmented VGs in this work.
  arXiv  Detail & Related papers  (2022-04-21T06:26:06Z)
• 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)
• Aesthetics, Personalization and Recommendation: A survey on Deep Learning in Fashion [3.202857828083949]
  The survey shows remarkable approaches that encroach the subject of achieving that by divulging deep into how visual data can be interpreted and leveraged. Aesthetics play a vital role in clothing recommendation as users' decision depends largely on whether the clothing is in line with their aesthetics, however the conventional image features cannot portray this directly. The survey also highlights remarkable models like tensor factorization model, conditional random field model among others to cater the need to acknowledge aesthetics as an important factor in Apparel recommendation.
  arXiv  Detail & Related papers  (2021-01-20T19:57:13Z)
• Garment Design with Generative Adversarial Networks [7.640010691467089]
  This paper explores the capabilities of generative adversarial networks (GAN) for automated attribute-level editing of design concepts. The experiments support the hypothesized potentials of GAN for attribute-level editing of design concepts.
  arXiv  Detail & Related papers  (2020-07-21T17:03:33Z)
• Designer Modeling through Design Style Clustering [4.4447051343759965]
  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.
  arXiv  Detail & Related papers  (2020-04-03T17:57:41Z)

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.