From Text to CAD: How Visual Feedback Revolutionizes Large Language Models in the Design Process

Creating Computer-Aided Design (CAD) models is a complex and time-consuming process that requires specialized expertise. Automating this process by converting text descriptions into CAD parameter sequences, known as Text-to-CAD, is a promising approach to increasing efficiency. Traditional methods focused on using ground-truth parameter sequences as the basis for training AI models. However, CAD models are inherently multimodal, consisting of both these sequences and the corresponding rendered visual objects. The challenge is that the rendering process from parameter sequences to visual objects is not unique, meaning different sequences can lead to the same visual result. Therefore, both sequential and visual signals are essential for effective training.

A new approach, discussed in current research, leverages the strengths of Large Language Models (LLMs) in combination with visual feedback to improve Text-to-CAD generation. This approach is based on the realization that LLMs are capable of capturing and processing complex relationships in text data. By integrating visual feedback, these models can learn how rendered visual objects are perceived and evaluated, thus optimizing the generation of CAD models.

CADFusion: A Two-Stage Training Approach

A promising framework that pursues this approach is CADFusion. This framework utilizes LLMs as a foundation and alternates between two training phases: the Sequential Learning phase (SL) and the Visual Feedback phase (VF). In the SL phase, the LLMs are trained with ground-truth parameter sequences to enable the generation of logically coherent sequences. In the VF phase, parameter sequences that lead to visually preferred objects are rewarded, while sequences that do not are penalized. This iterative process allows the LLMs to learn the relationship between parameter sequences and the resulting visual representations.

Alternating between these two phases ensures that both sequential and visual information are considered and the benefits of both signals are leveraged. Initial experiments show that CADFusion significantly improves performance both qualitatively and quantitatively. The generated CAD models are more precise and better match the textual descriptions.

The Importance of Multimodal Learning for the Future of CAD

The integration of visual feedback into LLMs for Text-to-CAD generation is a significant step towards more efficient and intuitive CAD modeling. By combining the strengths of LLMs with the ability to process visual information, new possibilities are opened for the automation and optimization of design processes. Future research could focus on improving the feedback mechanisms and expanding the scope of this approach.

Developments in the field of Text-to-CAD highlight the potential of multimodal learning for the future of artificial intelligence. By combining different data types, such as text and image, AI models can develop a deeper understanding of the world and handle more complex tasks. This opens up new perspectives for the application of AI in various fields, from product development to art and creativity.

Bibliography: - Wang, R., Yuan, Y., Sun, S., & Bian, J. (2025). Text-to-CAD Generation Through Infusing Visual Feedback in Large Language Models. arXiv preprint arXiv:2501.19054. - Liang, X., et al. (2024). Rich Human Feedback for Text-to-Image Generation. *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)*. - Xu, C. (n.d.). Coco Xu's Homepage. Retrieved from https://cocoxu.github.io/ - Diverse further publications and presentations at conferences like NeurIPS 2024 and platforms like OpenReview.net. - Research papers on related topics such as "Engineering Sketch Generation for Computer-Aided Design" and "Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works" on ResearchGate and ScienceDirect.