Pose Transfer
41 papers with code • 6 benchmarks • 5 datasets
Latest papers
Weakly-supervised 3D Pose Transfer with Keypoints
The main challenges of 3D pose transfer are: 1) Lack of paired training data with different characters performing the same pose; 2) Disentangling pose and shape information from the target mesh; 3) Difficulty in applying to meshes with different topologies.
Bidirectionally Deformable Motion Modulation For Video-based Human Pose Transfer
To address these issues, we propose a novel Deformable Motion Modulation (DMM) that utilizes geometric kernel offset with adaptive weight modulation to simultaneously perform feature alignment and style transfer.
Zero-shot Pose Transfer for Unrigged Stylized 3D Characters
We present a zero-shot approach that requires only the widely available deformed non-stylized avatars in training, and deforms stylized characters of significantly different shapes at inference.
MAPConNet: Self-supervised 3D Pose Transfer with Mesh and Point Contrastive Learning
Unsupervised methods have been proposed for graph convolutional models but they require ground truth correspondence between the source and target inputs.
UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer
Text-to-image models (T2I) such as StableDiffusion have been used to generate high quality images of people.
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.
Unsupervised 3D Pose Transfer with Cross Consistency and Dual Reconstruction
With $G$ as the basic component, we propose a cross consistency learning scheme and a dual reconstruction objective to learn the pose transfer without supervision.
SCAM! Transferring humans between images with Semantic Cross Attention Modulation
In this work, we introduce SCAM (Semantic Cross Attention Modulation), a system that encodes rich and diverse information in each semantic region of the image (including foreground and background), thus achieving precise generation with emphasis on fine details.
Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Textures
Then we reconstruct the input image by sampling from the permuted textures for patch-level disentanglement.
Cross Attention Based Style Distribution for Controllable Person Image Synthesis
In this paper, we propose a cross attention based style distribution module that computes between the source semantic styles and target pose for pose transfer.