DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces

Multimodal CAD generation faces a fundamental scalability challenge. Design-history methods are confined to small annotated datasets, while BRep topology is discrete and non-differentiable. Meanwhile, millions of unannotated 3D meshes remain untapped, since existing CAD methods cannot leverage them without explicit CAD annotations. We propose DreamCAD, a multimodal generative framework that bridges this gap by representing shapes as C0-continuous Bézier patches with differentiable tessellation, enabling direct point-level supervision on large-scale 3D meshes without CAD-specific annotations. We further introduce CADCap-1M, the largest CAD captioning dataset with 1M+ GPT-5-generated descriptions to advance text-to-CAD research. DreamCAD achieves state-of-the-art performance on ABC and Objaverse across text, image, and point modalities, surpassing 75% user preference. Finally, we show that DreamCAD’s accurate, compact geometry enables topology recovery into production-ready CAD models, exportable as editable STEP files. Project page is available at https://s adilkhan.github.io/dreamcad2026/.