Learning to generate chairs with convolutional neural networks
Open accessAbstract
We train a generative convolutional neural network which is able to generate images of objects given object type, viewpoint, and color. We train the network in a supervised manner on a dataset of rendered 3D chair models. Our experiments show that the network does not merely learn all images by heart, but rather finds a meaningful representation of a 3D chair model allowing it to assess the similarity of different chairs, interpolate between given viewpoints to generate the missing ones, or invent new chair styles by interpolating between chairs from the training set. We show that the network can be used to find correspondences between different chairs from the dataset, outperforming existing approaches on this task.
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Data augmentation with elastic deformations allows the network to learn invariance to such deformations without the need to see these transformations in the annotated image corpus.
“This allows the network to learn invariance to such deformations, without the need to see these transformations in the annotated image corpus.”
From U-Net: Convolutional Networks for Biomedical Image Segmentation (2015) · cites this paper as Dosovitskiy et al. [3]
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- Authors
- Dosovitskiy, A., Springenberg, J. T., & Brox, T.
- Affiliations
- University of Freiburg (DE)
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 1538-1546
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1893585201
- Cited by
- 640
- References
- 2
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 13 · 2023: 35 · 2022: 32 · 2021: 47 · 2020: 75 · 2019: 87 · 2018: 107 · 2017: 104 · 2016: 90 · 2015: 34 · 2014: 2
- FWCI
- 205.28
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- correspondence learning, object similarity
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