| id |
caadria2023_26 |
| authors |
Karsan, Zain |
| year |
2023 |
| title |
Desk Mate: A Collaborative Drawing Platform |
| doi |
https://doi.org/10.52842/conf.caadria.2023.2.521
|
| source |
Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 521–530 |
| summary |
Machine Learning (ML) in architecture is an emerging field with myriad potentials to impact the design process. Despite its many possibilities, ML is typically employed when the design problem is sufficiently defined, and further, is only integrated within software environments. Desk Mate is collaborative drawing machine that can be used early in the design process by coupling tangible tools like pens and trace paper with ML driven feedback and generation. Embedding physical tools that are familiar and intuitive with digital intelligence offers designers new ways of engaging with ML algorithms interactively, potentially changing the way the architectural industry approaches design problems. Desk Mate chains together image retrieval methods from machine vision with generative ML models like variational autoencoders (VAE) and generative adversarial networks (GANS) to react to design sketches as they are drawn. This pipeline allows Desk Mate to iterate through designs with the designer. Thus, Desk Mate demonstrates an interactive platform that collocates designer and machine as creative agents, facilitating drawing with ML driven feedback, potentially accelerating design iteration in the early stages of ideation. |
| keywords |
human machine interaction, machine learning and artificial intelligence, interactive machine learning, robotics and autonomous systems |
| series |
CAADRIA |
| email |
zkarsan@mit.edu |
| full text |
 file.pdf (2,667,774 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/06/15 23:14 |
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