21 papers with code • 2 benchmarks • 2 datasets
We propose a novel algorithm that relies on a weak form of supervision where the data is partitioned into sets according to certain inactive factors of variation.
This paper proposes a new generative adversarial network for pose transfer, i. e., transferring the pose of a given person to a target pose.
Ranked #1 on Pose Transfer on Market-1501
Specifically, given an image of a person and a target pose, we synthesize a new image of that person in the novel pose.
Ranked #5 on Gesture-to-Gesture Translation on NTU Hand Digit
We address the problem of guided image-to-image translation where we translate an input image into another while respecting the constraints provided by an external, user-provided guidance image.
Ranked #1 on Image Reconstruction on Edge-to-Clothes
Generating novel, yet realistic, images of persons is a challenging task due to the complex interplay between the different image factors, such as the foreground, background and pose information.
Ranked #2 on Gesture-to-Gesture Translation on Senz3D
Unlike existing methods, we propose to estimate dense and intrinsic 3D appearance flow to better guide the transfer of pixels between poses.
Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh.
We present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task.
Ranked #1 on Pose Transfer on Deep-Fashion