Generate Images from Lines Inredningsdesigngenerator av Junming Chen

Designen Generate Images from Lines Inredningsdesigngenerator av Junming Chen är en högt ansedd vinnare av den mycket prestigefyllda 2023 A' Design Award i Järn i tävlingskategorin Generativ, Algoritmisk, Parametrisk och AI-assisterad Design.

JärnGenerativ· 2023

Generate Images from Lines Inredningsdesigngenerator

av Junming Chen

Klient: Junming Chen

Vinnare av A' Design Award i Järn i kategorin Generativ

47,588

Verifierad prisvinnare

Verifiera prisstatus för #150690

Visa hos A' Design Award Pressmeddelande Utmärkelsemärke

Färger

Designen

Om Generate Images from Lines Inredningsdesigngenerator

Generate Images from Lines är ett artificiell intelligens-system som skapar inredningsdesignrenderingar från linjeritningar. Systemet bygger på en förbättrad diffusionsmodell, tränad på mer än 20 000 inredningsdesignbilder som samlats in och annoterats av professionella designers. Designers matar in ett handritat eller digitalt linjeutkast och väljer en dekorativ stil, till exempel modern, japansk, europeisk eller kinesisk. En rendering produceras på några sekunder. Designer ändras genom att redigera linjeutkastet, vilket innebär att manuell modellering och ny rendering inte krävs.

Erkännandet

Om A' Design Award i Järn

Generate Images from Lines Inredningsdesigngenerator är vinnare av A' Design Award i Järn, ett prestigefyllt erkännande som tilldelas bra designverk som uppfyller de professionella och industriella krav som förväntas av välgjorda skapelser. Järnvinnare visar stor skicklighet, hög specialisering, expertförståelse och skaparnas kreativa förmåga. Generate Images from Lines Inredningsdesigngenerator förväntas vara en högt skattad, beundrad och älskad skapelse som är praktisk och innovativ. Detta vinnande verk i A' Design Award i Järn integrerar branschens bästa praxis och har kompetenta tekniska egenskaper kopplade till kunnande och talang. Generate Images from Lines Inredningsdesigngenerator respekteras för sin grundlighet och är utformat för att åstadkomma förbättringar av livskvaliteten som hjälper till att göra världen till en bättre plats.

Bakom designen

Presskit

Erkännanden, teammedlemmar och tekniska detaljer tillhandahållna av designern.

Unika egenskaper: The design was done by artificial intelligence (AI) in collaboration with designers. We trained an AI that professionally generates interior designs, which can generate interior designs with different decorative styles based on input line drafts. It solves the problem that the traditional design modification requires remodeling and rendering. It only takes a few seconds to generate a design using this method, and the efficiency of generation and modification is increased by dozens of times.
Dimensioner & förpackning: Use artificial intelligence to cooperate with designers to generate designs with different decorative styles. The design area is 160 square meters, and the decoration styles include modern, Japanese, European, and Chinese styles.
Taggar: Artificial intelligence, Human-machine collaboration, Interior design, Generative design, Efficient design, Rapid modification, Controllable design, Decoration style control
Produktionsteknik: The AI technology we use is our improved diffusion model technology. The typical defect of the diffusion model is that it cannot generate design according to specific "decorative style" and "space attribute," which is fatal for interior design. To this end, we re-collected and manufactured a data set containing more than 20,000 interior design renderings and data annotations and handed the data set to AI learning to generate an AI suitable for interior design. The AI can generate interior renderings specifying "decorative style" and "space attributes" by inputting line sketches.
Designutmaning: 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, we 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.
Forskning: Research Background: The interior design process could be more convenient, and the efficiency of design and modification could be higher.
Methods: We trained an AI for professional interior design and used AI to cooperate with people to generate interior designs of various decorative styles from line drawings.
Results: Using our AI, designers can obtain an interior design rendering every few seconds, efficiently generating and modifying designs.
Insights: Our method can quickly generate large batches of interior designs with specified decoration styles and spatial attributes, and the generated results can be changed by modifying the line draft. Design efficiency is improved by eliminating the need for manual modeling and rendering.
Inspiration: The traditional process of obtaining renderings for interior design is tedious and time-consuming. We use artificial intelligence (AI) to generate interior design, improve design efficiency, and explore new ways of human-computer collaborative design. Specifically, we trained an AI to create interior designs, which can quickly generate and modify designs in cooperation with humans. Changing the design does not require modifying the model and re-rendering, which improves design efficiency.
Projektlängd: The project is located in Guangzhou, Guangdong Province, China, and was designed in 2023.
Arbetsflöde: 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 hand-drawn or design line draft into AI to specify the decoration style.
5. AI generates the interior design of the specified decoration style.
6. The designer adjusts the line draft of the unsatisfactory part of the design and then regenerates the design.
7. After adjusting the line draft and generating it multiple times, a good design is obtained.