Generate Images from Lines Generator Desain Interior oleh Junming Chen

Karya Generate Images from Lines Generator Desain Interior oleh Junming Chen adalah pemenang yang sangat dihormati dari 2023 A' Design Award Besi yang sangat bergengsi dalam Kategori Kompetisi Desain Generatif, Algoritmik, Parametrik dan Berbantuan AI.

BesiGeneratif· 2023

Generate Images from Lines Generator Desain Interior

oleh Junming Chen

Klien: Junming Chen

Pemenang A' Design Award Besi dalam Kategori Generatif

48,097

Pemenang Penghargaan Terverifikasi

Verifikasi Status Penghargaan untuk #150690

Lihat di A' Design Award Siaran Pers Lencana Penghargaan

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Desain

Tentang Generate Images from Lines Generator Desain Interior

Generate Images from Lines adalah sistem kecerdasan buatan yang menciptakan render desain interior dari gambar garis. Sistem ini didasarkan pada model difusi yang disempurnakan, dilatih dengan lebih dari 20.000 gambar desain interior yang dikumpulkan dan dianotasi oleh desainer profesional. Desainer memasukkan draf garis yang digambar tangan atau digital dan memilih gaya dekoratif, seperti modern, Jepang, Eropa, atau Tiongkok. Sebuah render dihasilkan dalam beberapa detik. Desain diubah dengan menyunting draf garis, sehingga pemodelan manual dan pengulangan proses render tidak diperlukan.

Pengakuan

Tentang A' Design Award Besi

Generate Images from Lines Generator Desain Interior adalah pemenang A' Design Award Besi, pengakuan yang bergengsi yang diberikan kepada desain yang baik yang memenuhi persyaratan profesional dan industri yang diharapkan dari kreasi yang dirancang dengan baik. Pemenang Besi menunjukkan keterampilan yang besar, spesialisasi yang tinggi, pemahaman ahli, dan kapasitas kreatif para penciptanya. Generate Images from Lines Generator Desain Interior diharapkan menjadi kreasi yang sangat dihargai, dikagumi, dan dicintai yang praktis dan inovatif. Desain pemenang A' Design Award Besi ini mengintegrasikan praktik terbaik industri dan memiliki karakteristik teknis yang kompeten yang terkait dengan pengetahuan teknis dan bakat. Generate Images from Lines Generator Desain Interior dihormati karena ketelitiannya dan dirancang untuk memberikan peningkatan kualitas hidup yang membantu menjadikan dunia tempat yang lebih baik.

Di Balik Desain

Kit Pers

Kredit, anggota tim, dan detail teknis yang disediakan oleh desainer.

Keunikan: 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.
Dimensi & Kemasan: 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.
Tag: Artificial intelligence, Human-machine collaboration, Interior design, Generative design, Efficient design, Rapid modification, Controllable design, Decoration style control
Teknologi Produksi: 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.
Tantangan Desain: 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.
Riset: 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.
Inspirasi: 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.
Durasi Proyek: The project is located in Guangzhou, Guangdong Province, China, and was designed in 2023.
Alur Operasi: 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.