Yuehan Zhu, Tomohiro Fukuda, Nobuyoshi Yabuki

A Mixed Reality Design System for Interior Renovation: Inpainting with 360-Degree Live Streaming and Generative Adversarial Networks after Removal

  • Computer Science (miscellaneous)

In contemporary society, “Indoor Generation” is becoming increasingly prevalent, and spending long periods of time indoors affects well-being. Therefore, it is essential to research biophilic indoor environments and their impact on occupants. When it comes to existing building stocks, which hold significant social, economic, and environmental value, renovation should be considered before new construction. Providing swift feedback in the early stages of renovation can help stakeholders achieve consensus. Additionally, understanding proposed plans can greatly enhance the design of indoor environments. This paper presents a real-time system for architectural designers and stakeholders that integrates mixed reality (MR), diminished reality (DR), and generative adversarial networks (GANs). The system enables the generation of interior renovation drawings based on user preferences and designer styles via GANs. The system’s seamless integration of MR, DR, and GANs provides a unique and innovative approach to interior renovation design. MR and DR technologies then transform these 2D drawings into immersive experiences that help stakeholders evaluate and understand renovation proposals. In addition, we assess the quality of GAN-generated images using full-reference image quality assessment (FR-IQA) methods. The evaluation results indicate that most images demonstrate moderate quality. Almost all objects in the GAN-generated images can be identified by their names and purposes without any ambiguity or confusion. This demonstrates the system’s effectiveness in producing viable renovation visualizations. This research emphasizes the system’s role in enhancing feedback efficiency during renovation design, enabling stakeholders to fully evaluate and understand proposed renovations.

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