Human pose estimation is a key step to action recognition. We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose joint detector. 3D pose estimation is challenging because multiple 3D poses may correspond to the same 2D pose after projection due to the lack of depth information. Moreover, current 2D pose estimators are usually inaccurate which may cause errors in the 3D estimation. We address the challenges in three ways: (i) We represent a 3D pose as a linear combination of a sparse set of bases learned from 3D human skeletons. (ii) We enforce limb length constraints to eliminate anthropomorphically implausible skeletons. (iii) We estimate a 3D pose by minimizing the @math -norm error between the projection of the 3D pose and the corresponding 2D detection. The @math -norm loss term is robust to inaccurate 2D joint estimations. We use the alternating direction method (ADM) to solve the optimization problem efficiently. Our approach outperforms the state-of-the-arts on three benchmark datasets.
Existing work on 3D pose estimation can be classified into four categories according to their inputs to the system, e.g. the image, image features, camera parameters, etc. The first class @cite_8 @cite_0 takes camera parameters as inputs. For example, @cite_8 represent a 3D pose by a skeleton model and parameterize the body parts by truncated cones. They estimate the rotation angles of body parts by minimizing the silhouette discrepancy between the model projections and the input image by applying Markov Chain Monte Carlo (MCMC). Simo- @cite_0 represent a 3D pose by a set of joint locations. They automatically estimate the 2D pose, model each joint by a Gaussian distribution, and propagate the uncertainty to 3D pose space. They sample a set of 3D skeletons from the space and learn a SVM to determine the most feasible one.
@cite_0 @cite_8
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Markerless 3D human pose detection from a single image is a severely underconstrained problem because different 3D poses can have similar image projections. In order to handle this ambiguity, current approaches rely on prior shape models that can only be correctly adjusted if 2D image features are accurately detected. Unfortunately, although current 2D part detector algorithms have shown promising results, they are not yet accurate enough to guarantee a complete disambiguation of the 3D inferred shape. In this paper, we introduce a novel approach for estimating 3D human pose even when observations are noisy. We propose a stochastic sampling strategy to propagate the noise from the image plane to the shape space. This provides a set of ambiguous 3D shapes, which are virtually undistinguishable from their image projections. Disambiguation is then achieved by imposing kinematic constraints that guarantee the resulting pose resembles a 3D human shape. We validate the method on a variety of situations in which state-of-the-art 2D detectors yield either inaccurate estimations or partly miss some of the body parts. This paper addresses the problem of estimating human body pose in static images. This problem is challenging due to the high dimensional state space of body poses, the presence of pose ambiguity, and the need to segment the human body in an image. We use an image generative approach by modeling the human kinematics, the shape and the clothing probabilistically. These models are used for deriving a good likelihood measure to evaluate samples in the solution, space. We adopt a data-driven MCMC framework for searching the solution space efficiently. Our observation data include the face, head-shoulders contour, skin color blobs, and ridges; and they provide evidences on the positions of the head, shoulders and limbs. To translate these inferences into pose hypotheses, we introduce the use of 'proposal maps', which is an efficient way of consolidating the evidence and generating 3D pose candidates during the MCMC search. As experimental results show, the proposed technique estimates the human 3D pose accurately on various test images.