DensePose
A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body.
Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. DensePose-RCNN is implemented in the Detectron framework and is powered by Caffe2.
In this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide notebooks to visualize the collected DensePose-COCO dataset and show the correspondences to the SMPL model.
Installation
Please find installation instructions for Caffe2 and DensePose in INSTALL.md, a document based on the Detectron installation instructions.
Inference-Training-Testing
After installation, please see GETTING_STARTED.md for examples of inference and training and testing.
Notebooks
Visualization of DensePose-COCO annotations:
See notebooks/DensePose-COCO-Visualize.ipynbto visualize the DensePose-COCO annotations on the images:
DensePose-COCO in 3D:
See notebooks/DensePose-COCO-on-SMPL.ipynb to localize the DensePose-COCO annotations on the 3D template (SMPL) model:
Visualize DensePose-RCNN Results:
See notebooks/DensePose-RCNN-Visualize-Results.ipynb to visualize the inferred DensePose-RCNN Results.
DensePose-RCNN Texture Transfer:
See notebooks/DensePose-RCNN-Texture-Transfer.ipynb to localize the DensePose-COCO annotations on the 3D template (SMPL) model:
