Wired Perspectives: Multi-View Wire Art Embraces Generative AI
Abstract
Creating multi-view wire art (MVWA) a static 3D sculpture with diverse interpretations from different viewpoints is a complex task even for skilled artists. In response we present DreamWire an AI system enabling everyone to craft MVWA easily. Users express their vision through text prompts or scribbles freeing them from intricate 3D wire organisation. Our approach synergises 3D Bezier curves Prim's algorithm and knowledge distillation from diffusion models or their variants (e.g. ControlNet). This blend enables the system to represent 3D wire art ensuring spatial continuity and overcoming data scarcity. Extensive evaluation and analysis are conducted to shed insight on the inner workings of the proposed system including the trade-off between connectivity and visual aesthetics.
Cite
Text
Qu et al. "Wired Perspectives: Multi-View Wire Art Embraces Generative AI." Conference on Computer Vision and Pattern Recognition, 2024. doi:10.1109/CVPR52733.2024.00588Markdown
[Qu et al. "Wired Perspectives: Multi-View Wire Art Embraces Generative AI." Conference on Computer Vision and Pattern Recognition, 2024.](https://mlanthology.org/cvpr/2024/qu2024cvpr-wired/) doi:10.1109/CVPR52733.2024.00588BibTeX
@inproceedings{qu2024cvpr-wired,
title = {{Wired Perspectives: Multi-View Wire Art Embraces Generative AI}},
author = {Qu, Zhiyu and Yang, Lan and Zhang, Honggang and Xiang, Tao and Pang, Kaiyue and Song, Yi-Zhe},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2024},
pages = {6149-6158},
doi = {10.1109/CVPR52733.2024.00588},
url = {https://mlanthology.org/cvpr/2024/qu2024cvpr-wired/}
}