Generate Images from Lines Interieurontwerpgenerator door Junming Chen

Het ontwerp Generate Images from Lines Interieurontwerpgenerator door Junming Chen is een hoog aangeschreven winnaar van de zeer prestigieuze 2023 A' Design Award in IJzer in de competitiecategorie Generatief, Algoritmisch, Parametrisch en AI-ondersteund Ontwerp.

IJzerGeneratief· 2023

Generate Images from Lines Interieurontwerpgenerator

door Junming Chen

Opdrachtgever: Junming Chen

Winnaar van de A' Design Award in IJzer in de categorie Generatief

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Het Ontwerp

Over Generate Images from Lines Interieurontwerpgenerator

Genereer Afbeeldingen uit Lijnen is een kunstmatige-intelligentiesysteem dat interieurontwerprenders creëert op basis van lijntekeningen. Het systeem is gebaseerd op een verbeterd diffusiemodel, getraind op meer dan 20.000 interieurontwerpafbeeldingen die zijn verzameld en geannoteerd door professionele ontwerpers. Ontwerpers voeren een handgetekende of digitale lijnschets in en kiezen een decoratieve stijl, zoals modern, Japans, Europees of Chinees. Binnen enkele seconden wordt een render gegenereerd. Ontwerpen worden aangepast door de lijnschets te bewerken, waardoor handmatig modelleren en opnieuw renderen niet nodig zijn.

De Erkenning

Over de A' Design Award in IJzer

Generate Images from Lines Interieurontwerpgenerator is winnaar van de A' Design Award in IJzer, een prestigieuze erkenning die wordt toegekend aan goede ontwerpen die voldoen aan de professionele en industriële eisen die worden verwacht van goed ontworpen creaties. Winnaars in IJzer tonen grote vaardigheid, hoge specialisatie, deskundig inzicht en het creatieve vermogen van hun makers. Generate Images from Lines Interieurontwerpgenerator wordt verwacht een hooggewaardeerde, bewonderde en gekoesterde creatie te zijn die praktisch en innovatief is. Dit ontwerp dat de A' Design Award in IJzer heeft gewonnen, integreert de beste praktijken uit de sector en heeft bekwame technische kenmerken die verbonden zijn met knowhow en talent. Generate Images from Lines Interieurontwerpgenerator wordt gerespecteerd om zijn grondigheid en is ontworpen om verbeteringen van de levenskwaliteit te bieden die helpen de wereld een betere plek te maken.

Achter het Ontwerp

Perskit

Credits, teamleden en technische details verstrekt door de ontwerper.

Unieke Eigenschappen: 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.
Afmetingen & Verpakking: 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.
Tags: Artificial intelligence, Human-machine collaboration, Interior design, Generative design, Efficient design, Rapid modification, Controllable design, Decoration style control
Productietechnologie: 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.
Ontwerpuitdaging: 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.
Onderzoek: 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.
Inspiratie: 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.
Projectduur: The project is located in Guangzhou, Guangdong Province, China, and was designed in 2023.
Werkproces: 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.