Unsupervised learning of probably symmetric deformable 3D objects from images in the wild (extended abstract)
We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination. In order to disentangle these components without supervision, we...
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| Médium: | Conference item |
| Jazyk: | English |
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International Joint Conferences on Artificial Intelligence Organization
2021
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