Generate Images from Lines 인테리어 디자인 제너레이터 by Junming Chen

Generate Images from Lines 인테리어 디자인 제너레이터 by Junming Chen is a highly esteemed winner of the very prestigious 2023 A' Design Award 철상 in 제너레이티브, 알고리즘, 파라메트릭 및 AI 보조 디자인 Competition Category.

철제너레이티브· 2023

Generate Images from Lines 인테리어 디자인 제너레이터

by Junming Chen

Client: Junming Chen

A' Design Award 철상 Winner in 제너레이티브 Category

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View at A' Design Award Press Release Award Badge

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The Design

About Generate Images from Lines 인테리어 디자인 제너레이터

선에서 이미지 생성(Generate Images from Lines)은 선 드로잉으로부터 인테리어 디자인 렌더링을 만들어내는 인공지능 시스템이다. 이 시스템은 개선된 확산(diffusion) 모델을 기반으로 하며, 전문 디자이너들이 수집하고 주석을 단 2만 장 이상의 인테리어 디자인 이미지로 학습되었다. 디자이너는 손으로 그린 스케치나 디지털 라인 드래프트를 입력하고 모던, 일본식, 유럽식, 중국식 등의 장식 스타일을 선택한다. 그러면 몇 초 만에 렌더링이 생성된다. 라인 드래프트를 수정하는 것만으로 디자인이 변경되므로, 수작업 모델링과 재렌더링이 필요하지 않다.

The Recognition

About the A' Design Award 철상

Generate Images from Lines 인테리어 디자인 제너레이터, 이 작품은 A' Design Award 철상 수상작으로, 잘 디자인된 창작물에 기대되는 전문적·산업적 요건을 충족하는 좋은 디자인에 수여되는 권위 있는 인정입니다. 철상 수상작은 창작자의 뛰어난 기술, 높은 전문성, 전문가다운 이해, 창의적 역량을 보여 줍니다. Generate Images from Lines 인테리어 디자인 제너레이터, 이 작품은 높이 평가받고 감탄과 사랑을 받는 창작물로서 실용적이고 혁신적일 것으로 기대됩니다. 이 철상 수상 디자인은 업계 모범 사례를 통합하며 노하우와 재능으로 연결된 유능한 기술적 특성을 갖추고 있습니다. Generate Images from Lines 인테리어 디자인 제너레이터, 이 작품은 철저함으로 존중받으며 삶의 질을 개선하여 세상을 더 나은 곳으로 만들도록 디자인되었습니다.

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.