This unique combination of text and shape guidance allows for increased control over the generation process.
The search for efficient neural network architectures has gained much focus in recent years, where modern architectures focus not only on accuracy but also on inference time and model size.
We present a generic image-to-image translation framework, pixel2style2pixel (pSp).
If we were to have a prior telling us the coarse location of text instances in the image and their approximate scale, we could have adaptively chosen which regions to process and how to rescale them, thus significantly reducing the processing time.
It has been recently shown that neural networks can recover the geometric structure of a face from a single given image.
In contrast, we propose to leverage the power of convolutional neural networks to produce a highly detailed face reconstruction from a single image.
Fast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications.
SEBOOST applies a secondary optimization process in the subspace spanned by the last steps and descent directions.