Pose Estimation is a general problem in Computer Vision where we detect the position and orientation of an object.
( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )
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In this paper, we address the challenging task of estimating 6D object pose from a single RGB image.
To tackle this problem for training the network, we make use of a pose estimation dataset, MS-COCO, and employ unsupervised adversarial instance-level domain adaptation for estimating the entire pose of occluded pedestrians.
As gait analysis with a single camera is much more challenging compared to previous works utilizing multi-cameras, a RGB-D camera or wearable sensors, we propose using vision-based human pose estimation approaches.
At the coarsest resolution, and in a manner similar to classical part-based approaches, we leverage the kinematic structure of the human body to propagate convolutional feature updates between the keypoints or body parts.
In this paper, we aim to reduce such errors by incorporating the distances between pixels and keypoints into our objective.
Our STMC network consists of a spatial multi-cue (SMC) module and a temporal multi-cue (TMC) module.
3D animation of humans in action is quite challenging as it involves using a huge setup with several motion trackers all over the person's body to track the movements of every limb.
In this paper, we propose a new architecture named Rotation-invariant Mixed Graphical Model Network (R-MGMN) to solve the problem of 2D hand pose estimation from a monocular RGB image.