Generate Images from Lines Ynterieurûntwerp Generator troch Junming Chen

Generate Images from Lines Ynterieurûntwerp Generator troch Junming Chen is in heech wurdearre winner fan de tige prestizjeuze 2023 A' Design Award yn Izer yn de kompetysje-kategory Generatyf, Algoritmysk, Parametrysk en AI-stipe Ûntwerp.

IzerGeneratyf· 2023

Generate Images from Lines Ynterieurûntwerp Generator

troch Junming Chen

Klant: Junming Chen

A' Design Award yn Izer Winner yn de kategory Generatyf

48,097

Ferifiearre Priiswinner

Ferifiearje Prisstatus foar #150690

Besjoch by A' Design Award Parseberjocht Priisbadge

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It Untwerp

Oer Generate Images from Lines Ynterieurûntwerp Generator

Generate Images from Lines is in keunstmjittige yntelliginsjesysteem dat ynterieurûntwerptekeningen makket fan lyntekeningen. It systeem is basearre op in ferbettere diffúzjemodel, traint op mear as 20.000 ynterieurûntwerpôfbyldingen dy't sammele en annotearre binne troch profesjonele ûntwerpers. Untwerpers fiere in hânmjittich tekene of digitale lynskets yn en kieze in dekoratyfstyl, lykas modern, Japansk, Europeesk of Sineesk. Binnen in pear sekonden wurdt in werjefte generearre. Untwerpen wurde oanpast troch de lynskets te bewurkjen, wêrtroch hânmjittich modellearjen en opnij renderjen net nedich is.

De Erkenning

Oer de A' Design Award yn Izer

Generate Images from Lines Ynterieurûntwerp Generator is in winner fan de A' Design Award yn Izer, in prestisjeuze erkenning foar goede ûntwerpen dy't foldogge oan de profesjonele en yndustriële easken dy't ferwachte wurde fan goed ûntwurpen skeppings. Izerwinners litte grutte feardigens, hege spesjalisaasje, deskundich begryp en kreatyf fermogen fan har makkers sjen. Fan Generate Images from Lines Ynterieurûntwerp Generator wurdt ferwachte dat it in tige heech achte, bewûndere en dierbere skepping is dy't praktysk en ynnovatyf is. Dit winnende ûntwerp fan de A' Design Award yn Izer yntegrearret de bêste praktiken fan de sektor en hat feardige technyske skaaimerken ferbûn mei kennis en talint. Generate Images from Lines Ynterieurûntwerp Generator wurdt respektearre foar syn yngeandens en is ûntwurpen om ferbetteringen fan de kwaliteit fan it libben te leverjen dy't helpe de wrâld ta in bettere plak te meitsjen.

Efter it Untwerp

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Credits, teamleden en technyske details levere troch de ûntwerper.

Unike Eigenskippen: 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.
Ofmjittingen & Ferpakking: 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.
Labels: Artificial intelligence, Human-machine collaboration, Interior design, Generative design, Efficient design, Rapid modification, Controllable design, Decoration style control
Produksjetechnology: 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.
Untwerpútdaging: 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.
Undersyk: 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.
Ynspiraasje: 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.
Projektdoer: The project is located in Guangzhou, Guangdong Province, China, and was designed in 2023.
Operaasjestreamferrin: 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.