Analysis of pairings of colors and materials of furnishings in interior design with a data-driven framework

Park, Bo Hyeon; Hyun, Kyung Hoon

doi:10.1093/jcde/qwac114

Cited by 11 publications

(5 citation statements)

References 36 publications

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“…Park et al proposed a framework based on object detection and so on to derive furniture pairing principles. These experiments confirmed that images with high fidelity values matched existing style descriptions, proving that this framework could be used for indoor style image retrieval [12]. Solah et al proposed an automatic adjustment of the texture and color of virtual indoor scene objects to match target emotions.…”

Section: Related Workmentioning

confidence: 61%

Interior Scene Coloring Design Model Combining Improved K-means and SAA

2024

SCPE

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Due to technological progress and changes in people’s aesthetic standards, traditional design models need to be constantly broken through, seeking more efficient and accurate design methods. Seeking effective design models to improve design efficiency and prediction accuracy is an important task. Therefore, this study proposes an indoor scene coloring design model that combines improved K-means clustering and simulated annealing algorithm for this important task. Based on the analysis of indoor scene coloring, particle swarm optimization algorithm is used to optimize K-means clustering to achieve color classification. Combined with simulated annealing algorithm, adaptive adjustment of lighting conditions is achieved to enhance the naturalness and realism of coloring. These results confirmed that the proposed method had the highest average F-value, with an average F-value of 92.524 and 143.601 on both datasets, respectively. The average ARI values were 0.361 and 0.897, respectively. The designed algorithm performed the best and converged faster than other three. Therefore, the proposed method can effectively ensure the consistency between the distribution of data objects after clustering and the actual situation. For indoor scene coloring design, it has important practical significance and provides new possible paths for improving design efficiency and prediction accuracy.

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Section: Related Workmentioning

confidence: 61%

Interior Scene Coloring Design Model Combining Improved K-means and SAA

2024

SCPE

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“…Another significant challenge in interior design is the attainment of aesthetically pleasing designs 1 , 4 . Interior designers must factor in elements such as spatial layout, color harmony, material choice, furniture arrangement, and lighting design, among others.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrating aesthetics and efficiency: AI-driven diffusion models for visually pleasing interior design generation

Chen,

Shao,

Zheng

et al. 2024

Sci Rep

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The interior design suffers from inefficiency and a lack of aesthetic appeal. With the development of artificial intelligence diffusion models, using text descriptions to generate aesthetically pleasing designs has emerged as a new approach to address these issues. In this study, we propose a novel method based on the aesthetic diffusion model, which can quickly generate visually appealing interior design based on input text descriptions while allowing for the specification of decorative styles and spatial functions. The method proposed in this study creates creative designs and drawings by computer instead of from designers, thus improving the design efficiency and aesthetic appeal. We demonstrate the potential of this approach in the field of interior design through our research. The results indicate that: (1) The method efficiently provides designers with aesthetically pleasing interior design solutions; (2) By modifying the text descriptions, the method allows for the rapid regeneration of design solutions; (3) Designers can apply this highly flexible method to other design fields through fine-tuning. (4) The method optimizes the workflow of interior design.

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“…Then, material mapping is assigned to the 3D model, lighting is arranged for the space, and renderings are obtained. Finally, the customer determines whether the design is suitable by observing the renderings [5]. The linear workflow requires designers to design step by step.…”

Section: Conventional Interior Design Processmentioning

confidence: 99%

“…Existing design automation mainly focuses on a particular process in the design, but fewer studies focus on end-to-end design [4,5]. This study achieves end-to-end generation of interior design by building a text-to-image diffusion model, thereby improving design efficiency and addressing the lack of creativity.…”

Section: Conventional Interior Design Processmentioning

confidence: 99%

“…There is a huge demand for interior design worldwide, but existing design approaches or methodologies may not fully meet these needs [1][2][3]. One reason for this phenomenon is that the interior design process is complicated, and frequent changes lead to low design efficiency [1,[3][4][5]. In addition, designers form fixed design methods to save time, resulting in a lack of innovation [1,6,7].…”

Section: Introductionmentioning

confidence: 99%

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Generating Interior Design from Text: A New Diffusion Model-Based Method for Efficient Creative Design

2023

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Because interior design is subject to inefficiency, more creativity is imperative. Due to the development of artificial intelligence diffusion models, the utilization of text descriptions for the generation of creative designs has become a novel method for solving the aforementioned problem. Herein, we build a unique interior decoration style dataset. Thus, we solve the problem pertaining to the need for datasets, propose a new loss function that considers the decoration style, and retrain the diffusion model using this dataset. The trained model learns interior design knowledge and can generate an interior design through text. The proposed method replaces the designer’s drawing with computer-generated creative design, thereby enhancing the design efficiency and creative generation. Specifically, the proposed diffusion model can generate interior design images of specific decoration styles and spatial functions end to end from text descriptions, and the generated designs are easy to modify. This novel and creative design method can efficiently generate various interior designs, promote the generation of creative designs, and enhance the design and decision-making efficiency.

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Analysis of pairings of colors and materials of furnishings in interior design with a data-driven framework

Cited by 11 publications

References 36 publications

Interior Scene Coloring Design Model Combining Improved K-means and SAA

Interior Scene Coloring Design Model Combining Improved K-means and SAA

Integrating aesthetics and efficiency: AI-driven diffusion models for visually pleasing interior design generation

Generating Interior Design from Text: A New Diffusion Model-Based Method for Efficient Creative Design

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