Skip to Main content Skip to Navigation
Journal articles

Recovering 3D human pose from monocular images

Ankur Agarwal 1 Bill Triggs 1
1 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : We describe a learning-based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labeling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descriptor vectors extracted automatically from image silhouettes. For robustness against local silhouette segmentation errors, silhouette shape is encoded by histogram-of-shape-contexts descriptors. We evaluate several different regression methods: ridge regression, relevance vector machine (RVM) regression, and support vector machine (SVM) regression over both linear and kernel bases. The RVMs provide much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. The loss of depth and limb labeling information often makes the recovery of 3D pose from single silhouettes ambiguous. To handle this, the method is embedded in a novel regressive tracking framework, using dynamics from the previous state estimate together with a learned regression value to disambiguate the pose. We show that the resulting system tracks long sequences stably. For realism and good generalization over a wide range of viewpoints, we train the regressors on images resynthesized from real human motion capture data. The method is demonstrated for several representations of full body pose, both quantitatively on independent but similar test data and qualitatively on real image sequences. Mean angular errors of 4-6° are obtained for a variety of walking motions.
Document type :
Journal articles
Complete list of metadata

Cited literature [31 references]  Display  Hide  Download


https://hal.inria.fr/inria-00548619
Contributor : Thoth Team <>
Submitted on : Monday, December 20, 2010 - 10:08:17 AM
Last modification on : Monday, December 28, 2020 - 3:44:02 PM
Long-term archiving on: : Monday, March 21, 2011 - 2:37:44 AM

Identifiers

Collections

IMAG | CNRS | INRIA | UGA

Citation

Ankur Agarwal, Bill Triggs. Recovering 3D human pose from monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2006, 28 (1), pp.44--58. ⟨10.1109/TPAMI.2006.21⟩. ⟨inria-00548619⟩

Share

Metrics

Record views

1099

Files downloads

2875