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3D pose estimation is a process of predicting the transformation of an object from a user-defined reference pose, given an image or a 3D scan. It arises in computer vision or robotics where the pose or transformation of an object can be used for alignment of a computer-aided design models, identification, grasping , or manipulation of the object.
Poser (and Poser Pro) is a figure posing and rendering 3D computer graphics program distributed by Bondware. [2] Poser is optimized for the 3D modeling of human figures.It enables beginners to produce basic animations and digital images, along with the extensive availability of third-party digital 3D models.
The typical articulated body pose estimation system involves a model-based approach, in which the pose estimation is achieved by maximizing/minimizing a similarity/dissimilarity between an observation (input) and a template model. Different kinds of sensors have been explored for use in making the observation, including the following:
Pose estimation problems can be solved in different ways depending on the image sensor configuration, and choice of methodology. Three classes of methodologies can be distinguished: Analytic or geometric methods: Given that the image sensor (camera) is calibrated and the mapping from 3D points in the scene and 2D points in the image is known.
Perspective-n-Point [1] is the problem of estimating the pose of a calibrated camera given a set of n 3D points in the world and their corresponding 2D projections in the image. The camera pose consists of 6 degrees-of-freedom (DOF) which are made up of the rotation (roll, pitch, and yaw) and 3D translation of the camera with respect to the world.
Example of a T-posing model in MakeHuman software.. In computer animation, a T-pose is a default posing for a humanoid 3D model's skeleton before it is animated. [1] It is called so because of its shape: the straight legs and arms of a humanoid model combine to form a capital letter T.
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