37 papers with code • 6 benchmarks • 5 datasets
These leaderboards are used to track progress in Pose Transfer
Most implemented papers
Pose Guided Person Image Generation
This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose.
Progressive Pose Attention Transfer for Person Image Generation
This paper proposes a new generative adversarial network for pose transfer, i. e., transferring the pose of a given person to a target pose.
Controllable Person Image Synthesis with Attribute-Decomposed GAN
This paper introduces the Attribute-Decomposed GAN, a novel generative model for controllable person image synthesis, which can produce realistic person images with desired human attributes (e. g., pose, head, upper clothes and pants) provided in various source inputs.
XingGAN for Person Image Generation
We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i. e., translating the pose of a given person to a desired one.
Composer: Creative and Controllable Image Synthesis with Composable Conditions
Recent large-scale generative models learned on big data are capable of synthesizing incredible images yet suffer from limited controllability.
Disentangled Person Image Generation
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.
Deformable GANs for Pose-based Human Image Generation
Specifically, given an image of a person and a target pose, we synthesize a new image of that person in the novel pose.
Dense Intrinsic Appearance Flow for Human Pose Transfer
Unlike existing methods, we propose to estimate dense and intrinsic 3D appearance flow to better guide the transfer of pixels between poses.
Guided Image-to-Image Translation with Bi-Directional Feature Transformation
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.
Neural Pose Transfer by Spatially Adaptive Instance Normalization
Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh.