Text2CAD: Designers can efficiently generate parametric CAD models from text prompts. The prompts can vary from abstract shape descriptions to detailed parametric instructions.
We propose Text2CAD as the first AI framework for generating parametric CAD designs using multi-level textual descriptions . Our main contributions are:
Our data annotation pipeline generates multi-level text prompts describing the construction workflow of a CAD model with varying complexities. We use a two-stage method -
We developed Text2CAD Transformer to transform natural language descriptions into 3D CAD models by deducing all its intermediate design steps autoregres- sively. Our model takes as input a text prompt \(T\) and a CAD subsequence \(\mathbf{C}_{1:t-1}\) of length \({t-1}\). The text embedding \(T_{adapt}\) is extracted from \(T\) using a pretrained BeRT Encoder followed by a trainable Adaptive layer. The resulting embedding \(T_{adapt}\) and the CAD sequence embedding \(F^0_{t-1}\) is passed through \(\mathbf{L}\) decoder blocks to generate the full CAD sequence in auto-regressive way.
We evaluated the performance of Text2CAD using two strategies.
This work was in parts supported by the EU Horizon Europe Framework under grant agreement 101135724
(LUMINOUS).
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