In contrast to previous approaches that either lack the ability to generalize to arbitrary identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the target face.
Ranked #1 on Face Swapping on FaceForensics++ (ID retrieval metric)
Convolution and self-attention are acting as two fundamental building blocks in deep neural networks, where the former extracts local image features in a linear way while the latter non-locally encodes high-order contextual relationships.
Ranked #43 on Instance Segmentation on COCO minival
Extensive experiments well demonstrate the effectiveness and feasibility of our framework in different image-translation tasks.
In order to facilitate the research on precipitation downscaling for deep learning, we present the first REAL (non-simulated) Large-Scale Spatial Precipitation Downscaling Dataset, RainNet, which contains 62, 424 pairs of low-resolution and high-resolution precipitation maps for 17 years.
Existing pencil sketch algorithms are based on texture rendering rather than the direct imitation of strokes, making them unable to show the drawing process but only a final result.
In contrast to great success of memory-consuming face editing methods at a low resolution, to manipulate high-resolution (HR) facial images, i. e., typically larger than 7682 pixels, with very limited memory is still challenging.
Furthermore, the results are with distinctive artistic style and retain the anisotropic semantic information.