Multi-Person 3D Human Pose Estimation from Monocular Images

1. Multi-Person 3D Human Pose Estimation from Monocular Images Rishabh Dabral IIT Bombay Rahul Mitra IIT Bombay NiteshBharadwaj IIT Bombay Abhishek Sharma Axogyan AI Ganesh Ramakrishnan IIT Bombay Arjun Jain Axogyan AI

2. Problem Statement • 3D Human Pose Estimation: • Given an image of a human, estimate the 3D positions of body joints in Euclidean space.

3. Problem Statement • 2D Human Pose Estimation: • Given an image of a human, estimate the positions of body joints in pixel coordinates.

4. Problem Statement • A Multi-Person 3D Pose Estimation: • Sub-linear time complexity w.r.t. the number of persons • In-the-wild performance

5. Proposal: HG-RCNN • Estimate the 2D heatmaps using an Hourglass-augmented Faster-RCNN • Lift the keypoints to 3D using a shallow 3D pose module. • Approximate the camera-relative positions of the 3D skeletons • Modular; does not require a multi-person 3D pose dataset. • Datasets: MSCOCO (multi-person 2D) and Human3.6M (single-person 3D)

6. Global Pose Approximation • Weak-Perspective Projection Assumption • Assume that the 2D pose is the scaled version of the orthographic projection of the global 3D pose: • How to find ? • Ratio of Sum-of-bone-lengths as the scale factor : • Y, X

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