Generate Images from Lines Generator Dispositionis Interioris ab Junming Chen

Opus Generate Images from Lines Generator Dispositionis Interioris a designatore Junming Chen est victor maxime honoratus praemii praestigiosissimi 2023 Praemium A' Design Award Ferreum in Categoria Certaminis Designatio Generativa.

FerrumDesignatio Generativa· 2023

Generate Images from Lines Generator Dispositionis Interioris

a Junming Chen

Cliens: Junming Chen

Victor praemii Praemium A' Design Award Ferreum in Categoria Designatio Generativa

48,097

Victor Praemii Verificatus

Verifica Statum Praemii pro #150690

Vide apud A' Design Award Communicatum Insigne Praemii

Colores

Designum

De Generate Images from Lines Generator Dispositionis Interioris

Generatio Imaginum ex Lineis est systema intelligentiae artificialis quod imagines dispositionis interioris ex delineationibus linearibus creat. Systema in exemplari diffusionis meliorato fundatur, quod ex plus quam 20,000 imaginibus dispositionis interioris, a designatoribus professionalibus collectis et annotatis, exercitatum est. Designatores schema lineare manu vel digitaliter factum inserunt et stilum ornamentalem eligunt, ut modernum, Iaponicum, Europaeum vel Sinicum. Intra paucas secundas imago perficitur. Formae mutantur ipso schemate lineari edito, ita ut nec exemplar manuale nec iterata imaginatio requiratur.

Agnitio

De Praemium A' Design Award Ferreum

Generate Images from Lines Generator Dispositionis Interioris victor est Praemii A' Design Award Ferreum, honoris amplissimi formis bonis dati, quae postulatis professionalibus et industrialibus ab creationibus bene formatis exspectatis satisfaciunt. Victores Ferrei magnam peritiam, altam specializationem, intellectum periti et facultatem creandi auctorum suorum ostendunt. Exspectatur Generate Images from Lines Generator Dispositionis Interioris creationem multum aestimatam, admiratam et carissimam, practicam et novam fore. Haec forma Ferreo victa optimas industriae consuetudines integrat et proprietates technicas idoneas, cum scientia et talento coniunctas, habet. Generate Images from Lines Generator Dispositionis Interioris ob diligentiam suam honoratur et ad vitae qualitatem emendandam formatum est, ut mundum meliorem reddere iuvet.

Post Designum

Fasciculus Nuntii

Merita, sodales gregis et singula technica a designatore praebita.

Proprietates Singulares: 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.
Dimensiones et Involucrum: 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.
Notulae: Artificial intelligence, Human-machine collaboration, Interior design, Generative design, Efficient design, Rapid modification, Controllable design, Decoration style control
Technologia Productionis: 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.
Provocatio Designandi: 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.
Investigatio: 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.
Inspiratio: 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.
Duratio Operis: The project is located in Guangzhou, Guangdong Province, China, and was designed in 2023.
Cursus Operationis: 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.