Generate Images from Lines Interjora Dezajno-Generatoro de Junming Chen

La dizajno Generate Images from Lines Interjora Dezajno-Generatoro de Junming Chen estas tre estimata gajninto de la prestiĝa 2023 Fera A' Design Award en la Konkursa Kategorio Genera, Algoritma, Parametra kaj AI-Helpata Dezajno.

FeroGenera· 2023

Generate Images from Lines Interjora Dezajno-Generatoro

de Junming Chen

Kliento: Junming Chen

Gajninto de Fera A' Design Award en la Kategorio Genera

48,097

Kontrolita Premiogajninto

Kontroli Premiostaton por #150690

Vidi ĉe A' Design Award Gazetara Komuniko Premia Insigno

Koloroj

La Dezajno

Pri Generate Images from Lines Interjora Dezajno-Generatoro

Generi Bildojn el Linioj estas artefarita intelekto-sistemo, kiu kreas interjorajn dezajno-bildigojn el liniaj skizoj. La sistemo baziĝas sur plibonigita difuzomodelo, trejnita per pli ol 20 000 interjoraj dezajno-bildoj kolektitaj kaj komentitaj de profesiaj dezajnistoj. Dezajnistoj enigas mandesegnitan aŭ ciferecan linian skizon kaj elektas ornaman stilon, kiel modernan, japanan, eŭropan aŭ ĉinan. Bildigo estas produktata post kelkaj sekundoj. Dezajnoj estas ŝanĝataj per redaktado de la linia skizo, tial mana modeligado kaj rebildigado ne necesas.

La Agnosko

Pri la Fera A' Design Award

Generate Images from Lines Interjora Dezajno-Generatoro estas gajninto de la Fera A' Design Award, prestiĝa rekono donata al bonaj dezajnoj, kiuj plenumas la profesiajn kaj industriajn postulojn atendatajn de bone dezajnitaj kreaĵoj. La Feraj gajnintoj montras grandan lertecon, altan specialiĝon, spertan komprenon kaj kreivan kapablon de siaj kreintoj. Oni atendas, ke Generate Images from Lines Interjora Dezajno-Generatoro estu tre estimata, admirata kaj ŝatata kreaĵo, kiu estas praktika kaj nova. Ĉi tiu dezajno gajninta la Feran A' Design Award integras plej bonajn industriajn praktikojn kaj havas kompetentajn teknikajn karakterizojn ligitajn kun scipovo kaj talento. Generate Images from Lines Interjora Dezajno-Generatoro estas respektata pro sia zorgemo kaj estas dezajnita por provizi plibonigojn de la vivokvalito, kiuj helpas fari la mondon pli bona loko.

Malantaŭ la Dezajno

Gazetara Dosiero

Kreditoj, teamanoj kaj teknikaj detaloj provizitaj de la dezajnisto.

Unikaj Ecoj: 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.
Dimensioj kaj Pakado: 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.
Etikedoj: Artificial intelligence, Human-machine collaboration, Interior design, Generative design, Efficient design, Rapid modification, Controllable design, Decoration style control
Produkta Teknologio: 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.
Dizajna Defio: 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.
Esplorado: 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.
Inspiro: 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.
Projekta Daŭro: The project is located in Guangzhou, Guangdong Province, China, and was designed in 2023.
Funkcia Fluo: 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.