Exploring the use of generative AI for material texturing in 3D interior design spaces
Material selection is important yet difficult in interior design, as designers need to consider technical factors beyond aesthetics, such as maintenance, sustainability, and costs that are often considered in later stages of the design process. As a result, making design changes due to unanticipated...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-11-01
|
| Series: | Frontiers in Computer Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2024.1493937/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850219327089803264 |
|---|---|
| author | Rgee Wharlo Gallega Yasuyuki Sumi |
| author_facet | Rgee Wharlo Gallega Yasuyuki Sumi |
| author_sort | Rgee Wharlo Gallega |
| collection | DOAJ |
| description | Material selection is important yet difficult in interior design, as designers need to consider technical factors beyond aesthetics, such as maintenance, sustainability, and costs that are often considered in later stages of the design process. As a result, making design changes due to unanticipated technical constraints in the later stages can be costly. We attempt to approach this problem by anticipating these as early as the conceptualization stage, where designers model and assign textures to their 3D scenes. To this end, our study explores the use of generative AI tools, namely ChatGPT and DALLE-2, in both texturing 3D scenes and selecting materials for interior design projects. Through a prototype, we evaluated the generative AI tools by conducting a user study with professional designers and students (n = 11). Based on creativity support (CSI), participants averaged a score of 72.82/100, while in task load (NASA-TLX), they scored 47.36/100. Based on qualitative feedback, designers could easily search and explore textures and materials while also receiving informative and contextually relevant suggestions on materials and colors from ChatGPT. However, these tools can be improved by fine-tuning on domain-specific datasets. Lastly, we analyze how designers interacted with these tools and reflect on how they can benefit from using generative AI in texturing and material selection in the interior design process. |
| format | Article |
| id | doaj-art-e5514c54829145e78c532e344d39e945 |
| institution | OA Journals |
| issn | 2624-9898 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computer Science |
| spelling | doaj-art-e5514c54829145e78c532e344d39e9452025-08-20T02:07:24ZengFrontiers Media S.A.Frontiers in Computer Science2624-98982024-11-01610.3389/fcomp.2024.14939371493937Exploring the use of generative AI for material texturing in 3D interior design spacesRgee Wharlo GallegaYasuyuki SumiMaterial selection is important yet difficult in interior design, as designers need to consider technical factors beyond aesthetics, such as maintenance, sustainability, and costs that are often considered in later stages of the design process. As a result, making design changes due to unanticipated technical constraints in the later stages can be costly. We attempt to approach this problem by anticipating these as early as the conceptualization stage, where designers model and assign textures to their 3D scenes. To this end, our study explores the use of generative AI tools, namely ChatGPT and DALLE-2, in both texturing 3D scenes and selecting materials for interior design projects. Through a prototype, we evaluated the generative AI tools by conducting a user study with professional designers and students (n = 11). Based on creativity support (CSI), participants averaged a score of 72.82/100, while in task load (NASA-TLX), they scored 47.36/100. Based on qualitative feedback, designers could easily search and explore textures and materials while also receiving informative and contextually relevant suggestions on materials and colors from ChatGPT. However, these tools can be improved by fine-tuning on domain-specific datasets. Lastly, we analyze how designers interacted with these tools and reflect on how they can benefit from using generative AI in texturing and material selection in the interior design process.https://www.frontiersin.org/articles/10.3389/fcomp.2024.1493937/fullgenerative AIhuman-AI co-creationmaterial selectiontexturesinterior design |
| spellingShingle | Rgee Wharlo Gallega Yasuyuki Sumi Exploring the use of generative AI for material texturing in 3D interior design spaces Frontiers in Computer Science generative AI human-AI co-creation material selection textures interior design |
| title | Exploring the use of generative AI for material texturing in 3D interior design spaces |
| title_full | Exploring the use of generative AI for material texturing in 3D interior design spaces |
| title_fullStr | Exploring the use of generative AI for material texturing in 3D interior design spaces |
| title_full_unstemmed | Exploring the use of generative AI for material texturing in 3D interior design spaces |
| title_short | Exploring the use of generative AI for material texturing in 3D interior design spaces |
| title_sort | exploring the use of generative ai for material texturing in 3d interior design spaces |
| topic | generative AI human-AI co-creation material selection textures interior design |
| url | https://www.frontiersin.org/articles/10.3389/fcomp.2024.1493937/full |
| work_keys_str_mv | AT rgeewharlogallega exploringtheuseofgenerativeaiformaterialtexturingin3dinteriordesignspaces AT yasuyukisumi exploringtheuseofgenerativeaiformaterialtexturingin3dinteriordesignspaces |