VIDES: Virtual Interior Design via Natural Language and Visual Guidance
Minh-Hien Le Chi-Bien Chu Khanh-Duy Le Tam V. Nguyen Minh-Triet Tran Trung-Nghia Le
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