Technology Inheritance Tlamachtiliztli Calchīhualiztli Chīhualōni ītēchcopa Junming Chen

Tlanāhuatīlli Technology Inheritance Tlamachtiliztli Calchīhualiztli Chīhualōni ītēchcopa Junming Chen cē cencā mahuizzō tēmacēhualōni īpan huēyi 2023 Mahuizolli A' Design Award Tepoztli īpan Yancuicchihualiztli, Tlapohualiztepoztli, Tlanahuatiltepoztli ihuan Tepoztlalnamiquiliztli-Palehuilli Tlachihchihualiztli Nēhuilīztli Tlamantli.

TepoztliYancuicchihualiztli· 2023

Technology Inheritance Tlamachtiliztli Calchīhualiztli Chīhualōni

īpal Junming Chen

Tlanāhuatīlli: Chen junming

Mahuizolli A' Design Award Tepoztli Tēmacēhualōni īpan Yancuicchihualiztli Tlamantli

45,702

Tlaneltīlīlli Tēmacēhualōni

Xictlaneltilia Tēmacēhualli ītēchcopa #150834

Xiquitta A' Design Award ītechcopa Āmatl Tēmachiyōtīliztli Tlamachiōtl

Tlapalli

Tlanēxtilistli

Ītechcopa Technology Inheritance Tlamachtiliztli Calchīhualiztli Chīhualōni

Tlamatiliztli Tlacāhuiliztli in ītōca mochīuh ītechpōhui yōllōtlamatiliztli tlamachtīlli tlein quichīhua calli īxco tlacencāhualiztli. Nī chīhualōni quichīhua calli īxcopa nepapan calchīhualiztli tlamachiyōtl in itech tlahtōlli tlein tēchīuhqui quitlālia. Cē cē tlācuilōlli mochīhua zan quēzquipa cāhuitl, īhuān tlacencāhualiztli huel mopatla in ihqueh mopatla tlahtōlli tēnōnōtzalli. Nī tēchīuhcāyōtl tlachīhualōc ihcuāc ōmocentlālīlōquê īhuān ōtēnahuatīlōquê mīlli calchīhualiztli tlācuilōlli, zātēpan ōmochīuh cualnēzcāyōtl in cē tlācuilōlōni tlamachiyōtl in yancuīc tlamatiliztli tlācuilōlli. Nī tlaneltilīliztli īca titēpalēhuiah in āmo huel māchiyoh ma āxcān quiāhcīcān cualli tlamatiliztli nezcāyōtl.

Tēixīmachōtiliztli

Ītechcopa Mahuizolli A' Design Award Tepoztli

Technology Inheritance Tlamachtiliztli Calchīhualiztli Chīhualōni ca in quicuic in Mahuizolli A' Design Award Tepoztli, tlamahuizoliztli tlen momaca in cualli dizayno in quitlalia in tlen monequi ipan tequitl ihuan tlachihualiztli in quimaca in cualli tlachihualli. In quicuicah Tepoztli quinextia in hueyi tlamatiliztli, toltecayotl ihuan ihuelitiliztli intlachihuanime. Ca quinequi in Technology Inheritance Tlamachtiliztli Calchīhualiztli Chīhualōni ca tlazohtli, mahuiztic, yancuic ihuan motequiuhtia. Ni dizayno in quicuic Tepoztli quihuelitia in tlamatiliztli ihuan quipia chicahualiztli tlen tepoztli. Technology Inheritance Tlamachtiliztli Calchīhualiztli Chīhualōni quicualtia in nemiliztli ihuan quihuelitia in cemanahuac ica cualli.

Tlanēxtilistli Ītēixpan

Āmatl Tlanextīliztli

Tlapōhualli, tēquitqueh īwan tēchmachilīztli ōquinmaca in tlanēxtīlīlli.

Cē Zan Ītēchcopa: This project trains an AI that professionally generates the appearance of buildings. The AI can generate buildings of different architectural styles according to the text entered by the user. The AI only takes a few seconds to generate the building's appearance, and users can recreate the design by changing the text description. That is, designers can obtain design concept proposals through AI cooperation and continuously optimize the design.
Huēyiliztli īhuān Tlacualtepēhualli: Generating building exteriors using a fine-tuned stable diffusion technique.
Tōcāitl: Artificial intelligence, Human-computer cooperation, Architectural appearance generation, Text generation design, Quick modification, Architectural style control
Tlachīhualiztli Māchiyōtl: The effect of using stable diffusion to produce architectural appearance could be better. The reason is that this mode needs to sufficiently understand the design skills of different designers, resulting in a messy appearance design and low quality. For this, fine-tuning the existing AI is essential. So this project reworked the data set, fine-tuned the critical stable diffusion, and made an AI to generate the specified architectural style to assist the junior designer in the design.
Tlanāhuatīlli Nētēcahuilīztli: The challenge of this design is to build a professional architectural design AI for generating high-quality and stylized designs. This project collected and marked thousands of architectural appearance renderings of dozens of well-known architects, created a data set of architectural appearance, and then handed the data set to AI for fine-tuning learning. This project builds an AI to generate a given building's exterior style.
Tlatēmōliztli: Research Background: It is difficult for beginners to design high-quality architectural appearances.
Methods: This project trained an AI to professionally generate building exteriors, which can contract with designers to complete architectural exterior designs.
Results: Using the AI trained in this project, the designer can generate an architectural appearance rendering in just a few seconds.
Insights: Using this method, the architectural appearance of a specified style can be generated and modified through text description, and the quality of the generated design is close to that of a professional designer. This method does not require manual modeling and rendering, which improves design efficiency and quality.
Tēyōllālīliztli: Most people measure the quality of a building by evaluating how it looks. Good architectural exterior design can bring attention and economic benefits. However, the exterior architectural design is complex, and the quality of architectural exteriors designed by excellent designers and beginners varies greatly. This design makes an artificial intelligence (AI) that can expertly generate building exteriors to help beginners achieve designs that approach the quality of master builders.
Tēquitl Cāhuitl: The project has already been completed in 2023.
Tēquitl Ōhuitl: 1. Professional designers collect and mark, and screen images of outstanding designer building exteriors.
2. Use the filtered data to train an AI that specializes in generating the exterior of a given architectural style.
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.