GANs have made significant strides since their inception in 2014, demonstrating remarkable capabilities in generating realistic audio and video mixes, as well as complex geometries. However, a persistent challenge lies in ensuring the accuracy of the generated results. While the visual appeal of these results may be convincing, they often fail to faithfully represent the genuine geometric properties they aim to emulate. A pertinent example is the simulation of paper folding and crumpling, where maintaining the inherent geometric characteristics of the sheet (e.g., preventing stretching) is crucial. Although deterministic simulations of paper deformation have been developed, comprehending and replicating them often necessitate an extensive understanding of material physics, mathematics, and computer graphics. One potential approach to address this challenge involves harnessing the power of GANs or other network architectures, such as variational autoencoders, to analyze 3D geometries and generate highly accurate representations. However, several hurdles must be overcome, including operating effectively within the 3D space and establishing a robust methodology to evaluate the fidelity of the output geometries. An intriguing application of such methods for generating 3D geometries lies in their ability to rapidly generate synthetic data with exceptional accuracy. This synthesized data can then be employed for training purposes in various domains, offering a valuable resource for enhancing learning algorithms and expanding their applicability.


  • State-of-the-art review on GANs and paper simulation
  • Generating paper geometries using simulation
  • Use GANs to generate geometries and designing accuracy and comparison methodology
  • Evaluating generated results from GANs and possible refinement strategies


  • 3D computer vision, Computer graphics, Geometric modelling
  • Deep Learning (TensorFlow, PyTorch, Keras)
  • C++ (OpenGL, OpenCV), Python, C#


  • Goodfellow, I., Pouget-Abadie, 2020. Generative adversarial networks. Communications of the ACM, 63(11), pp.139-144.
  • Narain, R., 2013. Folding and crumpling adaptive sheets. ACM Transactions on Graphics (TOG), 32(4), pp.1-8.
  • Smith, E.J. ,2017, October. Improved adversarial systems for 3d object generation and reconstruction. In Conference on Robot Learning (pp. 87-96). PMLR.
  • Xiao, H., 2017. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms.
  • Das, S., 2019. Dewarpnet: Single-image document unwarping with stacked 3d and 2d regression networks.

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