Pose Estimation is a general problem in Computer Vision where we detect the position and orientation of an object.
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Given the results of MTN, we adopt an occlusion-aware Re-ID feature strategy in the pose tracking module, where pose information is utilized to infer the occlusion state to make better use of Re-ID feature.
Estimating 3D human pose from monocular images demands large amounts of 3D pose and in-the-wild 2D pose annotated datasets which are costly and require sophisticated systems to acquire.
We present a joint 3D pose and focal length estimation approach for object categories in the wild.
While recent work has shown direct estimation techniques can be quite powerful, geometric tracking methods using point clouds can provide a very high level of 3D accuracy which is necessary for many robotic applications.
Fiducial markers have been playing an important role in augmented reality (AR), robot navigation, and general applications where the relative pose between a camera and an object is required.
This paper presents a hybrid real-time camera pose estimation framework with a novel partitioning scheme and introduces motion averaging to on-line monocular systems.
In this paper, we tackle the problem of 3D human shape estimation from single RGB images.