Generate Images from Lines Amrazê Çêkirina Sêwirana Hundir by Junming Chen

Generate Images from Lines Amrazê Çêkirina Sêwirana Hundir by Junming Chen is a highly esteemed winner of the very prestigious 2023 Xelata A' Design Award a Hesinîn in Dîzayna Afirandî, Algorîtmîk, Parametrîk û bi Alîkariya AI-ê Competition Category.

HesinAfirandî· 2023

Generate Images from Lines Amrazê Çêkirina Sêwirana Hundir

by Junming Chen

Client: Junming Chen

Xelata A' Design Award a Hesinîn Winner in Afirandî Category

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Verified Award Winner

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View at A' Design Award Press Release Award Badge

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The Design

About Generate Images from Lines Amrazê Çêkirina Sêwirana Hundir

Wêne Çêbike ji Xêzan pergalek îstîxbarata çêkirî ye ku ji xêzkêşiyan wêneyên sêwirana hundir çêdike. Pergal li ser bingeha modelek difûzyonê ya pêşketî ye, ku bi zêdetirî 20,000 wêneyên sêwirana hundir ên ku ji aliyê sêwirankarên pîşeyî ve hatine berhevkirin û nîşankirin, hatiye perwerdekirin. Sêwirankar xêzkêşiyek destan an dîjîtal têxin nav pergalê û şêwazek xemilandinê hildibijêrin, wek modern, Japonî, Ewropî an Çînî. Di çend saniyeyan de wêneyek tê çêkirin. Sêwiran bi guhertina xêzkêşiyê tên guherandin, ji ber vê yekê pêdivî bi modelkirina destan û ji nû ve wêneyandinê nîne.

The Recognition

About the Xelata A' Design Award a Hesinîn

Generate Images from Lines Amrazê Çêkirina Sêwirana Hundir xelatgirê Xelata A' Design Award a Hesinîn e, nasîneke bi prestîj ku ji sêwiranên baş re tê dayîn, yên ku pêdiviyên pîşeyî û pîşesaziyê yên ku ji afirandinên baş hatine sêwirandin têne hêvîkirin tînin cih. Xelatgirên Hesinînê jêhatîbûna mezin, pisporiya bilind, têgihiştina pisporî û kapasîteya afirîner a afirînerên xwe nîşan didin. Tê hêvîkirin ku Generate Images from Lines Amrazê Çêkirina Sêwirana Hundir afirandineke pir hatiye rêzgirtin, sitayişkirin û delal, pratîkî û nûjen be. Ev sêwirana bi Hesinînê serketî pratîkên çêtirîn ên pîşesaziyê yekgirtî dike û xwedî taybetmendiyên teknîkî yên jêhatî ye ku bi zanîn û jêhatîbûnê ve girêdayî ne. Generate Images from Lines Amrazê Çêkirina Sêwirana Hundir ji ber baldariya xwe tê rêzgirtin û hatiye sêwirandin da ku kalîteya jiyanê baştir bike û alîkariya çêtirkirina dinyayê bike.

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. 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.
Dimensions & Packaging: 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
Production Technology: 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.
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, 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.
Research: 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.
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 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.