@inproceedings{
li2024cad,
title={{CAD} Translator: An Effective Drive for Text to 3D Parametric Computer-Aided Design Generative Modeling},
author={Xueyang Li and Yu Song and Yunzhong Lou and Xiangdong Zhou},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=DN3722rnLd}
}
Computer-Aided Design (CAD) generative modeling is widely applicable in the fields of industrial engineering. Recently, text-to-3D generation has shown rapid progress in point clouds, mesh, and other non-parametric representations. On the contrary, text to 3D parametric CAD generative modeling is a practical task that has not been explored well, where its shape can be defined with several editable parametric command sequences. To investigate this, we design an encoder-decoder framework, namely CAD Translator, for incorporating the awareness of parametric CAD sequences into texts appropriately with only one-stage training. We first align texts and parametric CAD sequences via a Cascading Contrastive Strategy in the latent space, and then we propose CT-Mix to conduct the random mask operation on their embeddings separately to further get a fusion embedding via the linear interpolation. This can strengthen the connection between texts and parametric CAD sequences effectively. To train CAD Translator, we create a Text2CAD dataset with the help of Large Multimodal Model (LMM) for this practical task and conduct thorough experiments to demonstrate the effectiveness of our method.
CAD Translator: An Effective Drive for Text to 3D Parametric Computer-Aided Design Generative Modeling
Xueyang Li Yu Song Yunzhong Lou Xiangdong Zhou