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Found 8 papers, 1 papers with code
Adversarial Parametric Pose Prior
The Skinned Multi-Person Linear (SMPL) model can represent a human body by mapping pose and shape parameters to body meshes.
Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation
In this paper we propose an unsupervised feature extraction method to capture temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich latent vectors.
Self-supervised Segmentation via Background Inpainting
While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on.
GarNet++: Improving Fast and Accurate Static3D Cloth Draping by Curvature Loss
We introduce a two-stream deep network model that produces a visually plausible draping of a template cloth on virtual 3D bodies by extracting features from both the body and garment shapes.
Self-supervised Training of Proposal-based Segmentation via Background Prediction
While supervised object detection methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on.
Neural Scene Decomposition for Multi-Person Motion Capture
To this end, we introduce a self-supervised approach to learning what we call a neural scene decomposition (NSD) that can be exploited for 3D pose estimation.
GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping
We fuse these features with those extracted in parallel from the 3D body, so as to model the cloth-body interactions.
Learning Monocular 3D Human Pose Estimation from Multi-view Images
Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets.
