SketchDNN: Joint Continuous-Discrete Diffusion for CAD Sketch Generation
Published in International Conference on Machine Learning (ICML), 2025
SketchDNN is a generative model for synthesizing CAD sketches that jointly models both continuous parameters and discrete class labels through a unified continuous-discrete diffusion process. The core innovation is Gaussian-Softmax diffusion, where logits perturbed with Gaussian noise are projected onto the probability simplex via a softmax transformation, facilitating blended class modeling for high-fidelity CAD sketch generation.
Recommended citation: Sathvik Chereddy, John Femiani, "SketchDNN: Joint Continuous-Discrete Diffusion for CAD Sketch Generation." International Conference on Machine Learning (ICML), 2025.