A Survey of Personalized Interior Design

Abstract

Interior design is the core step of interior decoration, and it determines the overall layout and style of furniture. Traditional interior design is usually laborious and time‐consuming work carried out by professional designers and cannot always meet clients' personalized requirements. With the development of computer graphics, computer vision and machine learning, computer scientists have carried out much fruitful research work in computer‐aided personalized interior design (PID). In general, personalization research in interior design mainly focuses on furniture selection and floor plan preparation. In terms of the former, personalized furniture selection is achieved by selecting furniture that matches the resident's preference and style, while the latter allows the resident to personalize their floor plan design and planning. Finally, the automatic furniture layout task generates a stylistically matched and functionally complete furniture layout result based on the selected furniture and prepared floor plan. Therefore, the main challenge for PID is meeting residents' personalized requirements in terms of both furniture and floor plans. This paper answers the above question by reviewing recent progress in five separate but correlated areas, including furniture style analysis, furniture compatibility prediction, floor plan design, floor plan analysis and automatic furniture layout. For each topic, we review representative methods and compare and discuss their strengths and shortcomings. In addition, we collect and summarize public datasets related to PID and finally discuss its future research directions.

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

“…Equation ( 2) adds the style-aware reconstruct loss and style prior preservation loss to Equation (1) and uses interior design category information (i.e., decoration style and space function) as part of the loss function, where g pr refers to the control vector with interior design category information and Y pr relates to the data generated with the frozen pretrained diffusion model. The loss function encourages the model to retain the knowledge of the original model while learning the knowledge of the decoration style and space function so that the model can generate the specified decoration style and corresponding space.…”

“…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].…”

“…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]. Therefore, it is important to improve the efficiency of interior design and address the lack of innovation.…”

The Effects of Wooden Furniture Color, Floor Material, and Age on Design Evaluation, Visual Attention, and Emotions in Office Environments

“…A review indicated that the presence of solid wood in indoor environments reduces stress levels and improves indoor air quality, perceptions derived from the visual and tactile senses of wood [2]. Personalization studies in interior design mainly focus on furniture selection and floor plans [3]. In offices, desks and chairs are essential pieces of furniture, making the choice of office furniture crucial in interior design.…”