Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Iterative GANs for Rotating Visual Objects
Ysbrand Galama, Thomas Mensink
Feb 12, 2018 (modified: Jun 04, 2018)ICLR 2018 Workshop Submissionreaders: everyoneShow Bibtex
Abstract:We are interested in learning visual representations which allow for 3D manipulations of visual objects based on a single 2D image. We cast this into an image-to-image transformation task, and propose Iterative Generative Adversarial Networks (IterGANs) to learn a visual representation that can be used for objects seen in training, but also for never seen objects. Since object manipulation requires a full understanding of the geometry and appearance of the object, our IterGANs learn an implicit 3D model and a full appearance model of the object, which are both inferred from a single (test) image. Moreover, the intermediate generated images from IterGANs can be used by additional loss functions to increase the quality of all generated images without the need for additional supervision. Experiments on rotated objects show how iterGANs help with the generation process.
TL;DR:IterGANs use iterative generators to rotate visual objects. The intermediate images allow for adding additional loss functions.
Enter your feedback below and we'll get back to you as soon as possible.