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RI-Small: Human Shape and Pose from Images

Support provided by NSF

Description

This project will develop new approaches for recovering the three-dimensional (3D) shape and pose of the human body in images and video sequences. The methods will use a detailed 3D body model learned from laser range scans of over 2000 people. The approach will model the shape variation across people as well as the non-rigid shape variation due to changes in pose. The project will develop and test methods for robustly recovering the body shape in surveillance video sequences, in scenes with strong lighting, from collections of snapshots and in unconstrained television/film sequences. The recovered body model will be used to produce a variety of biometric measurements.

The majority of images and video sequences are of humans and recognizing people and their actions is a core problem in computer vision. The problem is challenging however because the human body is a complex, non-rigid, and articulated structure that can vary dramatically in pose, shape and appearance. Current methods focus on estimating human pose and typically ignore the problem of human shape estimation. This project will treat these problems together resulting in more robust solutions which will have a wide ranging impact in multiple disciplines. Human pose estimation is currently used in areas such as gait analysis, special effects, game development, human factors, and sports training to name a few. Robust video-based systems like the one developed here will extend the range of applications to home entertainment, elder care, autonomous vehicles and animal movement analysis. By extending previous methods to also estimate the three-dimensional shape of the human body in images and video sequences this project will enable additional applications in video forensics, surveillance, preventative medicine and special effects. More generally, methods like those developed here, that robustly recover the shape and pose of people in complex images and video streams, will be applicable to a wider range of problems in object recognition and tracking.

Principal Investigator

Projects Supported

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