Zichun Shao ᱨᱮᱱᱟᱜ Only Text ᱡᱮᱱᱡᱮᱱ ᱤᱱᱴᱮᱨᱤᱭᱚᱨ ᱰᱤᱡᱟᱭᱤᱱ ᱦᱟᱛᱤᱭᱟᱨ ᱫᱚ ᱡᱮᱱᱟᱨᱮᱴᱤᱵ ᱯᱨᱚᱛᱤᱡᱚᱜᱤᱛᱟ ᱵᱷᱟᱜ ᱨᱮ 2023 A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱨᱮᱱᱟᱜ ᱟᱹᱰᱤ ᱢᱟᱱ ᱟᱠᱟᱱ ᱡᱤᱛᱠᱟᱨ ᱠᱟᱱᱟ᱾
ᱞᱚᱦᱟᱡᱮᱱᱟᱨᱮᱴᱤᱵ· 2023
Only Text ᱡᱮᱱᱡᱮᱱ ᱤᱱᱴᱮᱨᱤᱭᱚᱨ ᱰᱤᱡᱟᱭᱤᱱ ᱦᱟᱛᱤᱭᱟᱨ
Zichun Shao ᱞᱮᱠᱟᱛᱮ
ᱜᱟᱦᱟᱠ: Zichun Shao
ᱡᱮᱱᱟᱨᱮᱴᱤᱵ ᱵᱷᱟᱜ ᱨᱮ A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱡᱤᱛᱠᱟᱨ
ᱡᱟᱸᱪ ᱟᱠᱟᱱ ᱮᱣᱟᱨᱰ ᱡᱤᱛᱠᱟᱨ
#150725 ᱞᱟᱜᱤᱫ ᱮᱣᱟᱨᱰ ᱦᱟᱞᱚᱛ ᱡᱟᱸᱪᱰᱤᱡᱟᱭᱤᱱ
Only Text ᱡᱮᱱᱡᱮᱱ ᱤᱱᱴᱮᱨᱤᱭᱚᱨ ᱰᱤᱡᱟᱭᱤᱱ ᱦᱟᱛᱤᱭᱟᱨ ᱵᱟᱵᱚᱛ ᱛᱮ
ᱳᱱᱞᱤ ᱴᱮᱠᱥᱴ ᱢᱮᱱᱛᱮ ᱢᱤᱫ ᱡᱮᱱᱡᱮᱱ ᱰᱤᱡᱟᱭᱤᱱ ᱦᱟᱛᱤᱭᱟᱨ ᱠᱟᱱᱟᱭ ᱚᱠᱟ ᱚᱞ ᱟᱠᱟᱱ ᱵᱟᱭᱟᱱ ᱠᱷᱚᱱ ᱤᱱᱴᱮᱨᱤᱭᱚᱨ ᱰᱤᱡᱟᱭᱤᱱ ᱨᱮᱭᱟᱜ ᱪᱤᱛᱟᱹᱨ ᱛᱮᱭᱟᱨ ᱠᱟᱛᱮᱭᱟᱭ ᱾ ᱱᱚᱶᱟ ᱦᱟᱛᱤᱭᱟᱨ ᱫᱤᱯᱷᱭᱩᱡᱚᱱ ᱢᱚᱰᱮᱞ (diffusion model) ᱨᱮᱭᱟᱜ ᱵᱤᱱᱳᱸᱫ ᱨᱮ ᱵᱟᱱᱟᱣ ᱟᱠᱟᱱᱟ, ᱡᱟᱦᱟᱸ ᱯᱨᱚᱯᱷᱮᱥᱚᱱᱟᱞ ᱤᱱᱴᱮᱨᱤᱭᱚᱨ ᱰᱤᱡᱟᱭᱤᱱ ᱞᱟᱹᱜᱤᱫ ᱛᱨᱮᱱᱤᱝ ᱮᱢ ᱟᱠᱟᱱᱟ ᱾ ᱒᱐,᱐᱐᱐ ᱠᱚᱴᱮ ᱴᱟᱲᱟᱝ ᱤᱱᱴᱮᱨᱤᱭᱚᱨ ᱨᱮᱱᱰᱚᱨᱤᱝ (renderings) ᱟᱠᱷᱲᱟ ᱮᱫ ᱠᱟᱛᱮ ᱰᱤᱡᱟᱭᱤᱱᱚᱨ ᱠᱚ ᱫᱟᱨᱟᱭ ᱮᱛᱦᱚᱵ ᱟᱠᱟᱱᱟ ᱡᱟᱦᱟᱸ ᱛᱨᱮᱱᱤᱝ ᱰᱟᱴᱟ ᱥᱮᱴ (training data set) ᱛᱮᱭᱟᱨ ᱮᱫᱟᱭ ᱾ ᱳᱱᱞᱤ ᱴᱮᱠᱥᱴ ᱚᱠᱟᱛᱮ ᱡᱟᱦᱟᱱ ᱥᱟᱡᱟᱣ ᱥᱴᱟᱭᱤᱞ (decoration style) ᱟᱨ ᱡᱟᱭᱜᱟ ᱨᱮᱭᱟᱜ ᱜᱩᱬᱤ (space attributes) ᱠᱚ ᱵᱟᱪᱷᱟᱣ ᱠᱟᱱᱟ, ᱚᱱᱟ ᱠᱚ ᱛᱮ ᱡᱚᱛᱚ ᱩᱛᱮᱭ ᱥᱮᱠᱮᱱᱰ ᱨᱮ ᱢᱤᱫ ᱤᱱᱴᱮᱨᱤᱭᱚᱨ ᱫᱮᱠᱷᱟᱣ ᱛᱮᱭᱟᱨ ᱫᱟᱲᱮᱟᱭᱟ ᱾ ᱰᱮᱴᱮᱞ ᱠᱚ ᱴᱮᱠᱥᱴ ᱮᱰᱤᱴ ᱠᱟᱛᱮ ᱟᱨ ᱫᱚᱦᱲᱟ ᱛᱮᱭᱟᱨ ᱠᱟᱛᱮ ᱵᱚᱫᱚᱞ ᱟᱠᱟᱱᱟ ᱾ ᱱᱚᱶᱟ ᱛᱚᱨᱤᱠᱟ ᱫᱚ ᱦᱟᱛ ᱛᱮ ᱢᱚᱰᱮᱞᱤᱝ ᱟᱨ ᱨᱮᱱᱰᱚᱨᱤᱝ ᱨᱮᱭᱟᱜ ᱜᱟᱦᱤᱨ ᱵᱟᱹᱭᱟᱹᱨ ᱨᱮᱢᱮᱫ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ, ᱡᱟᱦᱟᱸ ᱛᱮ ᱟᱨᱦᱚᱸ ᱰᱷᱮᱨ ᱚᱯᱥᱚᱱ ᱠᱚ ᱡᱟᱛᱟᱹ ᱛᱮ ᱠᱞᱟᱭᱮᱸᱴ ᱠᱚ ᱥᱟᱶ ᱦᱟᱛᱮᱡ ᱟᱹᱰᱤ ᱞᱚᱜᱚᱱ ᱧᱟᱢ ᱮᱫᱟᱜ-ᱟ ᱾
ᱢᱟᱱᱟᱱ
A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱵᱟᱵᱚᱛ ᱛᱮ
Only Text ᱡᱮᱱᱡᱮᱱ ᱤᱱᱴᱮᱨᱤᱭᱚᱨ ᱰᱤᱡᱟᱭᱤᱱ ᱦᱟᱛᱤᱭᱟᱨ ᱫᱚ A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱧᱟᱢ ᱟᱠᱟᱱᱟ, ᱱᱚᱶᱟ ᱫᱚ ᱵᱷᱟᱞᱟᱭ ᱰᱤᱡᱟᱭᱤᱱ ᱠᱚ ᱞᱟᱹᱜᱤᱫ ᱮᱢ ᱟᱠᱟᱱ ᱥᱚᱢᱟᱱ ᱠᱟᱱᱟ᱾ ᱱᱚᱶᱟ ᱠᱚ ᱫᱚ ᱰᱤᱡᱟᱭᱤᱱᱟᱨ ᱠᱚ ᱨᱮᱭᱟᱜ ᱵᱷᱟᱵᱱᱟ ᱟᱨ ᱵᱷᱟᱞᱟᱭ ᱠᱟᱢᱤ ᱡᱷᱟᱞᱠᱟᱣ ᱠᱚ᱾ Only Text ᱡᱮᱱᱡᱮᱱ ᱤᱱᱴᱮᱨᱤᱭᱚᱨ ᱰᱤᱡᱟᱭᱤᱱ ᱦᱟᱛᱤᱭᱟᱨ ᱫᱚ ᱠᱚᱞᱟ, ᱣᱤᱜᱭᱟᱱ, ᱰᱤᱡᱟᱭᱤᱱ ᱟᱨ ᱴᱮᱠᱱᱚᱞᱚᱡᱤ ᱨᱮᱭᱟᱜ ᱥᱤᱢᱟ ᱵᱟᱲᱦᱟᱣ ᱟᱨ ᱫᱩᱱᱤᱭᱟ ᱵᱷᱟᱞᱟᱭ ᱞᱟᱹᱜᱤᱫ ᱜᱚᱲᱚ ᱮᱢ ᱠᱟᱱᱟ᱾
ᱰᱤᱡᱟᱭᱤᱱ ᱯᱤᱪᱷᱟ ᱨᱮ
ᱯᱨᱮᱥ ᱠᱤᱴ
ᱰᱤᱡᱟᱭᱤᱱᱟᱨ ᱧᱮᱞ ᱠᱷᱚᱱ ᱠᱨᱮᱰᱤᱴ, ᱴᱤᱢ ᱥᱫᱚᱥᱭ ᱟᱨ ᱴᱮᱠᱱᱤᱠᱮᱞ ᱵᱤᱵᱚᱨᱚᱬ ᱮᱢ ᱟᱠᱟᱱᱟ᱾
- ᱟᱞᱜᱟ ᱜᱩᱱ ᱠᱚ
- The design was done by artificial intelligence (AI) in collaboration with designers. This project has trained an AI for professional interior design generation, which can generate interior designs with different decoration styles according to the input text. It only takes a few seconds for AI to generate a new interior design, and it can quickly generate a large number of designs for communication with users, thereby improving design efficiency.
- ᱢᱟᱯ ᱟᱨ ᱯᱮᱠᱮᱡᱤᱝ
- The project uses diffusion model technology, using artificial intelligence to cooperate with designers to generate designs of different decorative styles. We retrained a professional interior design AI to quickly create designs with varying decoration styles.
- ᱴᱮᱜ ᱠᱚ
- Artificial intelligence, Human-computer collaboration, Interior design, Generative design, Text generative design, Efficient design, Rapid modification, Controllable design, Decoration style control
- ᱛᱮᱭᱟᱨ ᱛᱮᱠᱱᱚᱞᱚᱡᱤ
- Ordinary AI cannot generate interior design according to specific "decoration style" and "space attributes," which is a fatal flaw for interior design. To this end, this project collected more than 20,000 interior design renderings, labeled them to make a data set and gave the data set to AI for learning, thus creating an AI suitable for interior design. The AI can generate interior renderings specifying "decorative style" and "space attributes" by inputting text.
- ᱰᱤᱡᱟᱭᱱ ᱪᱮᱞᱮᱧ
- 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, this project 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.
- ᱜᱚᱵᱮᱥᱚᱱᱟ
- Research Background: Interior design communication is complex, and obtaining designs and communicating with customers quickly is necessary.
Methods: This project trained an AI to generate interior designs professionally and used the AI to collaborate with humans to create interior designs in various decorative styles from the text.
Results: Using the AI trained in this project, designers can generate an interior design rendering in just a few seconds.
Insights: This project approach rapidly generates large batches of interior designs for specified decor styles and spatial properties and allows new designs to be generated by modifying text. This method eliminates the need for manual modeling and rendering, which improves design efficiency. - ᱯᱨᱮᱨᱚᱱᱟ
- It is difficult for users to imagine the scheme described by the designer in words. Designers need to turn thinking into design quickly. With the development of artificial intelligence (AI), designers can generate designs directly from text. Specifically, this project trained an AI to create interior design, which can generate interior design only by inputting text, improving communication efficiency between designers and users.
- ᱯᱨᱚᱡᱮᱠᱴ ᱥᱚᱢᱚᱭ
- The project is located in Guangzhou, Guangdong Province, China, and was designed in 2023.
- ᱠᱟᱹᱢᱤ ᱦᱚᱨ
- 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 design text description into the AI to specify the decoration style.
5. AI generates the interior design of the specified decoration style.
6. Modify the unsatisfactory details in the design through text description, and regenerate the design.
7. After adjusting the text description and generating it multiple times, a good design is obtained.
ᱥᱯᱚᱱᱥᱚᱨᱰ
Designers.org ᱠᱷᱚᱱ ᱯᱟᱨᱚᱢ ᱛᱟᱞᱟᱥ
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ᱟᱨᱦᱚᱸ ᱠᱷᱚᱡ ᱢᱮ
ᱥᱟᱹᱜᱟᱹᱭ ᱰᱤᱡᱟᱭᱤᱱ ᱠᱚ
ᱢᱤᱫ ᱞᱮᱠᱟ ᱠᱮᱴᱮᱜᱚᱨᱤ ᱠᱷᱚᱱ ᱟᱨᱦᱚᱸ ᱯᱩᱨᱥᱠᱟᱨ ᱧᱟᱢᱟᱠᱟᱱ ᱰᱤᱡᱟᱭᱤᱱ ᱠᱚ ᱧᱮᱞ ᱢᱮ, ᱡᱚᱜᱭᱟᱛᱟ ᱧᱮᱞ ᱛᱮ ᱵᱟᱪᱷᱟᱣ ᱟᱠᱟᱱᱟ᱾
