| id |
caadria2023_347 |
| authors |
Li, Keke, Wu, Hao, Ding, Xiangwen, Li, Haowei and Yuan, Philip F. |
| year |
2023 |
| title |
Bespoke 3D Printed Chair: Research on the Digital Design and Fabrication Method of Multi-Body Pose Fusion |
| doi |
https://doi.org/10.52842/conf.caadria.2023.1.169
|
| 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. 169–178 |
| summary |
Customization has become a vital part of the post-industrial production model. Traditional production methods struggle with the challenge of collecting large amounts of data and the high costs of customization. With advancements in big data and deep learning algorithms, it is now possible to reduce the difficulty and cost of data collection, resulting in more accurate individual customization. This paper presents a proposed workflow for customizing multi-position seating for individuals. Using deep learning algorithms such as OpenPose and parametric design platforms such as Grasshopper, the workflow transforms user-generated photos of the body into a seating model that fits corresponding positions. This process combines deep learning algorithms, simplifies the data collection and processing process, and provides an interface for user interaction on the Grasshopper platform. The workflow provides a comprehensive example of data-driven customization in the context of big data. It explores the potential of a new paradigm in digital design where data is the primary driving force. |
| keywords |
Customization, Posture recognition, Multi-platform collaboration, Deep Learning, Workflow |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,512,020 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/06/15 23:14 |
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