Graphics, Imaging, and Games Lab

  • Home
  • About Us
  • Contact
  • Login
Home

From the Lab

  • Benchmark
  • Projects
  • Publications
  • Image Galleries
  • Blogs
  • Lab Members
  • Prospective Students
  • Search

Generalized Autoencoder for Volumetric Shape Generation

Yanran Guan
Tansin Jahan
Oliver van Kaick
Generalized Autoencoder for Volumetric Shape Generation. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.
Conference Paper

Related Projects
High­level Shape Representations for Content Creation

Download the PDF

https://doi.org/10.1109/CVPRW50498.2020.00142

Abstract

We introduce a 3D generative shape model based on the generalized autoencoder (GAE). GAEs learn a manifold latent space from data relations explicitly provided during training. In our work, we train a GAE for volumetric shape generation from data similarities derived from the Chamfer distance, and with a loss function which is the combination of the traditional autoencoder loss and the GAE loss. We show that this shape model is able to learn more meaningful structures for the latent manifolds of different categories of shapes, and provides better interpolations between shapes when compared to previous approaches such as autoencoders and variational autoencoders.


BibTeX

@inproceedings{guan2020generalized,
    author = {Yanran Guan and Tansin Jahan and Oliver van Kaick},
    title = {Generalized Autoencoder for Volumetric Shape Generation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
    pages = {1082--1088},
    year = 2020
}

  • Home
  • About Us
  • Contact
  • Login