文件名称：Efficient Human Pose Estimation from Single Depth Images
文件大小：8.29MB
文件格式：PDF
更新时间：2016-01-14 15:13:35
PAMI 2012, 人体姿态估计
微软研究院的最新大作。 We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body jointsfromasingledepthimage,withoutusinganytemporalinformation.Thekeytobothapproachesistheuseofalarge,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-realtime 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.