Image Shape Manipulation from a Single Augmented Training Sample
This addresses the challenge of image editing for users with limited data, though it appears incremental as it builds on existing generative models with a focus on single-image training.
The paper tackles the problem of conditional image manipulation using only a single training image, achieving remarkable performance by leveraging extensive augmentation with thin-plate-spline and mapping from primitive representations like edges or segmentations.
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. We find that extensive augmentation is key for enabling single image training, and incorporate the use of thin-plate-spline (TPS) as an effective augmentation. Our network learns to map between a primitive representation of the image to the image itself. The choice of a primitive representation has an impact on the ease and expressiveness of the manipulations and can be automatic (e.g. edges), manual (e.g. segmentation) or hybrid such as edges on top of segmentations. At manipulation time, our generator allows for making complex image changes by modifying the primitive input representation and mapping it through the network. Our method is shown to achieve remarkable performance on image manipulation tasks.