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VIDES: Virtual Interior Design via Natural Language and Visual Guidance

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Minh-Hien Le, Chi-Bien Chu, Khanh-Duy Le, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le

Computer Vision and Pattern Recognition · Submitted 26 Aug 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Consequently, this system can potentially reduce the entry barrier for indoor design, making it more accessible to users with limited technical skills and reducing the time required to create high-quality images.

how it is stated · hedged possibility 1.00 read the record →

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arXiv ID2308.13795 (version 1)
Pith IDVZDIRRVG, Pith's permanent record of this paper and its review
CitationChi-Bien Chu, Khanh-Duy Le, Minh-Hien Le, Minh-Triet Tran, Tam V. Nguyen, Trung-Nghia Le. "VIDES: Virtual Interior Design via Natural Language and Visual Guidance." arXiv:2308.13795 (2023). https://pith.science/paper/VZDIRRVG
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,777 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.11 · human participants 0.16 · controlled comparison 0.06 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.08 · mentions code or data availability 0.45.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Interior design is crucial in creating aesthetically pleasing and functional indoor spaces. However, developing and editing interior design concepts requires significant time and expertise. We propose Virtual Interior DESign (VIDES) system in response to this challenge. Leveraging cutting-edge technology in generative AI, our system can assist users in generating and editing indoor scene concepts quickly, given user text description and visual guidance. Using both visual guidance and language as the conditional inputs significantly enhances the accuracy and coherence of the generated scenes, resulting in visually appealing designs. Through extensive experimentation, we demonstrate the effectiveness of VIDES in developing new indoor concepts, changing indoor styles, and replacing and removing interior objects. The system successfully captures the essence of users' descriptions while providing flexibility for customization. Consequently, this system can potentially reduce the entry barrier for indoor design, making it more accessible to users with limited technical skills and reducing the time required to create high-quality images. Individuals who have a background in design can now easily communicate their ideas visually and effectively present their design concepts. https://sites.google.com/view/ltnghia/research/VIDES

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Interior design is crucial in creating aesthetically pleasing and functional indoor spaces. background 1.00 0.04 0.02 asserted 1.00
s1 However, developing and editing interior design concepts requires significant time and expertise. background 0.81 0.06 0.02 asserted 1.00
s2 We propose Virtual Interior DESign (VIDES) system in response to this challenge. contribution 1.00 0.73 0.05 asserted 0.99
s3 Leveraging cutting-edge technology in generative AI, our system can assist users in generating and editing indoor scene concepts quickly, given user text description and visual guidance. contribution 0.56 0.66 0.10 asserted 0.86
s4 Using both visual guidance and language as the conditional inputs significantly enhances the accuracy and coherence of the generated scenes, resulting in visually appealing designs. result 0.87 0.77 0.30 asserted 1.00
s5 Through extensive experimentation, we demonstrate the effectiveness of VIDES in developing new indoor concepts, changing indoor styles, and replacing and removing interior objects. result 0.94 0.96 0.25 asserted 1.00
s6 The system successfully captures the essence of users' descriptions while providing flexibility for customization. result 0.71 0.72 0.14 asserted 1.00
s7 Consequently, this system can potentially reduce the entry barrier for indoor design, making it more accessible to users with limited technical skills and reducing the time required to create high-quality images. interpretation 1.00 0.24 0.61 hedged possibility 1.00
s8 Individuals who have a background in design can now easily communicate their ideas visually and effectively present their design concepts. https://sites.google.com/view/ltnghia/research/VIDES interpretation 0.95 0.13 0.18 asserted 0.91

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.95.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Interior design is crucial in creating aesthetically pleasing and functional indoor spaces. However, developing and editing interior design concepts requires significant time and expertise. We propose Virtual Interior DESign (VIDES) system in response to this challenge. Leveraging cutting-edge technology in generative AI, our system can assist users in generating and editing indoor scene concepts quickly, given user text description and visual guidance. Using both visual guidance and language as the conditional inputs significantly enhances the accuracy and coherence of the generated scenes, resulting in visually appealing designs. Through extensive experimentation, we demonstrate the effectiveness of VIDES in developing new indoor concepts, changing indoor styles, and replacing and removing interior objects. The system successfully captures the essence of users' descriptions while providing flexibility for customization. Consequently, this system can potentially reduce the entry barrier for indoor design, making it more accessible to users with limited technical skills and reducing the time required to create high-quality images. Individuals who have a background in design can now easily communicate their ideas visually and effectively present their design concepts. https://sites.google.com/view/ltnghia/research/VIDES

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2 arXiv papers cite this in 2 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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