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Generative design of decorative architectural parts

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Abstract

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.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Yuzhe Zhang, Chan Chi Ong, Jianmin Zheng & Zhendong Guo

  2. School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore

    Seng-Tjhen Lie

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  1. Yuzhe Zhang
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  2. Chan Chi Ong
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  3. Jianmin Zheng
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  4. Seng-Tjhen Lie
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  5. Zhendong Guo
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Correspondence to Jianmin Zheng.

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This work is supported by the Ministry of Education, Singapore, under its MoE Tier-2 Grant (2017-T2-1-076).

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Zhang, Y., Ong, C.C., Zheng, J. et al. Generative design of decorative architectural parts. Vis Comput 38, 1209–1225 (2022). https://doi.org/10.1007/s00371-021-02142-1

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