3d pose estimation tensorflow

1. What is 3D object pose estimation?
2. What is the difference between MediaPipe and TensorFlow?
3. What is TensorFlow Posenet?
4. What is MediaPipe pose estimation?

What is 3D object pose estimation?

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.

What is the difference between MediaPipe and TensorFlow?

As shown in the performance tables below, the MediaPipe runtime provides faster inference speed on desktop, laptop and android phones. The TensorFlow. js runtime provides faster inference speed on iPhones and iPads.

What is TensorFlow Posenet?

In simple words, Posenet is a deep learning TensorFlow model that allows you o estimate human pose by detecting body parts such as elbows, hips, wrists, knees, ankles, and form a skeleton structure of your pose by joining these points.

What is MediaPipe pose estimation?

MediaPipe Pose is a single-person pose estimation framework. It uses BlazePose 33 landmark topology. BlazePose is a superset of COCO keypoints, Blaze Palm, and Blaze Face topology. It works in two stages – detection and tracking.