This paper presents a method for generative design of decorative architectural parts such as corbel,moulding and panel, which
usually have clear structure and aesthetic details. The method is composed of two components: offline learning and online
generation. The offline learning trains a 2D CurveInfoGAN and a 3D VoxelVAE that learn the feature representations of the
parts in a dataset. The online generation proceeds with an evolution procedure that evolves to product new generation of
part components by selecting, crossing over and mutating features, followed by a feature-driven deformation that synthesizes
the 3D mesh representation of new models. Built upon these technical components, a generative design tool is developed,
which allows the user to input a decorative architectural model as a reference and then generates a set of new models that
are “more of the same” as the reference and meanwhile exhibit some “surprising” elements. The experiments demonstrate
the effectiveness of the method and also showcase the use of classic geometric modelling and advanced machine learning
techniques in modelling of architectural parts.