title: Efficient human pose estimation from single depth images.
abstract: We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body joints from a single depth image without using any temporal information. The key to both approaches is the use of a large, realistic, and highly varied synthetic set of training images. This allows us to learn models that are largely invariant to factors such as pose, body shape, field-of-view cropping, and clothing. Our first approach employs an intermediate body parts representation, designed so that an accurate per-pixel classification of the parts will localize the joints of the body. The second approach instead directly regresses the positions of body joints. By using simple depth pixel comparison features and parallelizable decision forests, both approaches can run super-real time on consumer hardware. Our evaluation investigates many aspects of our methods, and compares the approaches to each other and to the state of the art. Results on silhouettes suggest broader applicability to other imaging modalities.
authors.affiliation: Microsoft Research, Cambridge.
authors.forename: Jamie
authors.initials: J
authors.lastname: Shotton
authors.forename: Ross
authors.initials: R
authors.lastname: Girshick
authors.forename: Andrew
authors.initials: A
authors.lastname: Fitzgibbon
authors.forename: Toby
authors.initials: T
authors.lastname: Sharp
authors.forename: Mat
authors.initials: M
authors.lastname: Cook
authors.forename: Mark
authors.initials: M
authors.lastname: Finocchio
authors.forename: Richard
authors.initials: R
authors.lastname: Moore
authors.forename: Pushmeet
authors.initials: P
authors.lastname: Kohli
authors.forename: Antonio
authors.initials: A
authors.lastname: Criminisi
authors.forename: Alex
authors.initials: A
authors.lastname: Kipman
authors.forename: Andrew
authors.initials: A
authors.lastname: Blake
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D021621:Imaging, Three-Dimensional; D012044:Regression Analysis