Contents
What is DensePose?
DensePose, is Facebook’s real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body. Research in human understanding aims primarily at localizing a sparse set of joints, like the wrists, or elbows of humans.
How do I install DensePose?
Installing DensePose
- Requirements.
- Notes.
- Install Dependencies. Python Requirements (via Conda) COCO API. PyTorch & Caffe2. Install from Source Code. Install from Binaries via Conda. Install protobuf package. Caffe2 Installation Test.
- DensePose. Install DensePose. Fetch DensePose data.
- Setting-up the COCO dataset.
- Acknowledgements.
What is dense pose estimation?
Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. We propose DensePose-RCNN, a variant of Mask-RCNN, to densely regress part-specific UV coordinates within every human region at multiple frames per second.
Is DensePose open source?
Today, Facebook AI Research (FAIR) open sourced DensePose, our real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body. For these tasks, a more complete, surface-based image interpretation is required. …
What is camera pose?
Camera pose is referred to the position and orientation of a camera with respect to a reference coordinate system, which is usually known as the world coordinate system.
What is the use of pose estimation?
Introduction. Pose estimation is a computer vision technique that predicts and tracks the location of a person or object. This is done by looking at a combination of the pose and the orientation of a given person/object.
What is open pose?
OpenPose is the first real-time multi-person system to jointly detect human body, hand, facial, and foot key-points (in total 135 key-points) on single images. It was proposed by researchers at Carnegie Mellon University. They have released in the form of Python code, C++ implementation and Unity Plugin.
What does densepose do in real time on Facebook?
DensePose, is Facebook’s real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body. Research in human understanding aims primarily at localizing a sparse set of joints, like the wrists, or elbows of humans.
How is densepose used in Facebook AI research?
Today, Facebook AI Research (FAIR) open sourced DensePose, our real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body. Recent research in human understanding aims primarily at localizing a sparse set of joints, like the wrists, or elbows of humans.
What is the purpose of the densepose project?
The DensePose project addresses this and aims at understanding humans in images in terms of such surface-based models. Our work shows that one can efficiently compute dense correspondences between 2D RGB images and 3D surface models for the human body.
How is densepose used in human pose estimation?
DensePose, dense human pose estimation, is designed to map all human pixels of an RGB image to a 3D surface-based representation of the human body. This is done through the introduction of a large-scale, manually annotated dataset, and a variant of Mask-RCNN, a simple, flexible framework for object instance segmentation.