A generative A.I. technique for structure and texture transfer in furniture design

Clicks: 1
ID: 285501
2021
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #3,101 of 3,757 articles by views in Malay Journal

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Recently, generative AI methods such as style transfer and generative adversarial networks (GANs) have made significant advancements in the general area of design, where outputs (i.e. shoes, buildings, bags) are generated with near-realistic quality. Furthermore there are studies that have applied these methods into the area of furniture design. Generative models including GANs enable synthesizing new furniture and interpolation of structural features between furniture; on the other hand, style transfer allows transferring the textures and colors from one furniture to another. Based on style transfer, we propose a generative AI technique that performs both texture transfer and structure transfer from furniture images onto 3D furniture models. Our technique was evaluated on transferring textural and structural features from a dataset of Filipino designer furniture images onto chair and table models from the ShapeNet dataset. Experimental results showed that our texture transfer method outperformed all baselines in texture synthesis based on Wasserstein Distance. We recommend further investigation on improving image-to-model structure transfer, and also on ways for texture transfer to be more applicable in the industry. Keywords: Texture Transfer, Texture Synthesis, Structure Transfer, Style Transfer, Furniture Design, Generative AI, Convolutional Neural Networks, Deep Learning
Reference Key
persistent_1760655793_68f179b147a9a Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gallega, Rgee Wharlo
Journal Malay Journal
Year 2021
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdm_comsci/2
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.