We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input.
The edge generator hallucinates edges of the missing region (both regular and irregular) of the image, and the image completion network fills in the missing regions using hallucinated edges as a priori.
In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data.
Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal.
Motivated by these observations, we propose a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions.
Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value).
We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching.
SOTA for Image Generation on CIFAR-10
To this end, the encoder feature of the known region is shifted to serve as an estimation of the missing parts.