Only Text Generatives Innenarchitektur-Tool by Zichun Shao

Only Text Generatives Innenarchitektur-Tool by Zichun Shao is a highly esteemed winner of the very prestigious 2023 A' Design Award in Eisen in Generatives Design Competition Category.

EisenGenerativ· 2023

Only Text Generatives Innenarchitektur-Tool

by Zichun Shao

Client: Zichun Shao

A' Design Award in Eisen Winner in Generativ Category

43,980

Verified Award Winner

Verify Award Status for #150725

View at A' Design Award Press Release Award Badge

Colors

The Design

About Only Text Generatives Innenarchitektur-Tool

Only Text ist ein generatives Designwerkzeug, das aus schriftlichen Beschreibungen Bilder für die Innenarchitektur erzeugt. Das Tool basiert auf einem Diffusionsmodell, das speziell für professionelle Innenarchitektur trainiert wurde. Mehr als 20.000 Innenraum-Renderings wurden gesammelt und von Designern annotiert, um den Trainingsdatensatz zu bilden. Only Text kann innerhalb weniger Sekunden eine Innenansicht mit einem gewählten Einrichtungsstil und definierten Raumeigenschaften erzeugen. Details lassen sich verändern, indem der Text bearbeitet und die Ansicht erneut generiert wird. Das Verfahren macht manuelles Modellieren und Rendern überflüssig, sodass Kunden schnell zahlreiche Varianten präsentiert werden können.

The Recognition

About the A' Design Award in Eisen

Only Text Generatives Innenarchitektur-Tool ist Gewinner des A' Design Award in Eisen, einer prestigeträchtigen Anerkennung für gute Designs, die die beruflichen und industriellen Anforderungen erfüllen, die an gut gestaltete Schöpfungen gestellt werden. Eisen-Gewinner zeigen großes Können, hohe Spezialisierung, fachkundiges Verständnis und die kreative Kapazität ihrer Schöpfer. Von Only Text Generatives Innenarchitektur-Tool wird erwartet, dass es eine hochgeschätzte, bewunderte und geliebte Schöpfung ist, die praktisch und innovativ ist. Dieses mit dem A' Design Award in Eisen prämierte Design integriert bewährte Branchenverfahren und weist kompetente technische Merkmale in Verbindung mit Know-how und Talent auf. Only Text Generatives Innenarchitektur-Tool wird für seine Gründlichkeit geschätzt und ist darauf ausgelegt, Verbesserungen der Lebensqualität zu liefern, die dazu beitragen, die Welt zu einem besseren Ort zu machen.

Behind the Design

Press Kit

Credits, team members and technical details provided by the designer.

Unique Properties: The design was done by artificial intelligence (AI) in collaboration with designers. This project has trained an AI for professional interior design generation, which can generate interior designs with different decoration styles according to the input text. It only takes a few seconds for AI to generate a new interior design, and it can quickly generate a large number of designs for communication with users, thereby improving design efficiency.
Dimensions & Packaging: The project uses diffusion model technology, using artificial intelligence to cooperate with designers to generate designs of different decorative styles. We retrained a professional interior design AI to quickly create designs with varying decoration styles.
Tags: Artificial intelligence, Human-computer collaboration, Interior design, Generative design, Text generative design, Efficient design, Rapid modification, Controllable design, Decoration style control
Production Technology: Ordinary AI cannot generate interior design according to specific "decoration style" and "space attributes," which is a fatal flaw for interior design. To this end, this project collected more than 20,000 interior design renderings, labeled them to make a data set and gave the data set to AI for learning, thus creating an AI suitable for interior design. The AI can generate interior renderings specifying "decorative style" and "space attributes" by inputting text.
Design Challenge: The challenge of this design is to build a professional interior design AI that generates designs of high quality and correct decor. To this end, this project asked professional designers to collect and label tens of thousands of interior design images and then handed over the filtered data to AI for training. Make AI understand what is "decorative style," "spatial attributes," and "aesthetic attributes" and generate an interior design with a specified decorative style.
Research: Research Background: Interior design communication is complex, and obtaining designs and communicating with customers quickly is necessary.
Methods: This project trained an AI to generate interior designs professionally and used the AI to collaborate with humans to create interior designs in various decorative styles from the text.
Results: Using the AI trained in this project, designers can generate an interior design rendering in just a few seconds.
Insights: This project approach rapidly generates large batches of interior designs for specified decor styles and spatial properties and allows new designs to be generated by modifying text. This method eliminates the need for manual modeling and rendering, which improves design efficiency.
Inspiration: It is difficult for users to imagine the scheme described by the designer in words. Designers need to turn thinking into design quickly. With the development of artificial intelligence (AI), designers can generate designs directly from text. Specifically, this project trained an AI to create interior design, which can generate interior design only by inputting text, improving communication efficiency between designers and users.
Project Duration: The project is located in Guangzhou, Guangdong Province, China, and was designed in 2023.
Operation Flow: 1. Professional designers collect interior design images and mark and screen them.
2. Use the filtered data to train a professional interior design AI.
3. Designers use AI to design.
4. The designer inputs the design text description into the AI to specify the decoration style.
5. AI generates the interior design of the specified decoration style.
6. Modify the unsatisfactory details in the design through text description, and regenerate the design.
7. After adjusting the text description and generating it multiple times, a good design is obtained.