Although existing artistic style transfer methods have achieved significant improvement with
deep neural networks, they still suffer from artifacts such as disharmonious colors and …

Image inpainting has made remarkable progress with recent advances in deep learning.
Popular networks mainly follow an encoder-decoder architecture (sometimes with skip …

Implicit neural representations store videos as neural networks and have performed well for
vision tasks such as video compression and denoising. With frame index and/or positional …

We present a high-fidelity 3D generative adversarial network (GAN) inversion framework
that can synthesize photo-realistic novel views while preserving specific details of the input …

Image and video inpainting is a classic problem in computer vision and computer graphics,
aiming to fill in the plausible and realistic content in the missing areas of images and videos …

The deep learning-based image inpainting models discussed in this review are critical
image processing techniques for filling in missing or removed regions in static planar …

Image inpainting requires filling the corrupted regions with plausible contents and fitting
seamlessly with the surroundings. Currently, CNN-dominated inpainting models still …

We present a novel approach to reference-based super-resolution (RefSR) with the focus on
dual-camera super-resolution (DCSR), which utilizes reference images for high-quality and …

We propose a novel framework for video inpainting by adopting an internal learning
strategy. Unlike previous methods that use optical flow for cross-frame context propagation …

We scrutinise an important observation plaguing scene-level sketch research--that a
significant portion of scene sketches are" partial". A quick pilot study reveals:(i) a scene …