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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We propose a novel Nonparametric Structure Regularization Machine (NSRM) for 2D hand pose estimation, adopting a cascade multi-task architecture to learn hand structure and keypoint representations jointly.
Compared to a unitary representation, our hybrid representation allows pose regression to exploit more and diverse features when one type of predicted representation is inaccurate (e. g., because of occlusion).
Most 3d human pose estimation methods assume that input -- be it images of a scene collected from one or several viewpoints, or from a video -- is given.
Solving 6D pose estimation is non-trivial to cope with intrinsic appearance and shape variation and severe inter-object occlusion, and is made more challenging in light of extrinsic large illumination changes and low quality of the acquired data under an uncontrolled environment.
In this paper, we introduce a novel RGB-D based relative pose estimation approach that is suitable for small-overlapping or non-overlapping scans and can output multiple relative poses.
In this paper, we propose to benchmark action recognition methods in the absence of context.
In this work, we propose a multi-task framework for jointly estimating 2D or 3D human poses from monocular color images and classifying human actions from video sequences.
We perform extensive simulation and real-world experiments to validate the efficacy of our approach and compare the performance with state-of-art approaches.