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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This setting allows varied kinematic structures within a semantic category, and multiple instances to co-exist in an observation of real world.
We present a new trainable system for physically plausible markerless 3D human motion capture, which achieves state-of-the-art results in a broad range of challenging scenarios.
In this work, we propose MarkerPose, a robust, real-time pose estimation system based on a planar target of three circles and a stereo vision system.
Additionally, we fine-tune methods on AGORA and show improved performance on both AGORA and 3DPW, confirming the realism of the dataset.
Our method starts by extracting a set of potential 2D locations for the joints of both hands as extrema of a heatmap.
In this work, we propose PoseC3D, a new approach to skeleton-based action recognition, which relies on a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons.
Furthermore, these methods suffer from limited accuracy and temporal instability due to ambiguities caused by the monocular setup and the severe occlusion in a strongly distorted egocentric perspective.
We present a new multi-stream 3D mesh reconstruction network (MSMR-Net) for hand pose estimation from a single RGB image.
Skeletor can achieve this as it implicitly learns the spatio-temporal context of human motion via a transformer based neural network.