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.
The fourth class @cite_10 @cite_9 requires only a single image or image features ( e.g. silhouettes). For example, @cite_10 match a test image to the stored exemplars using shape context descriptors, and transfer the matched 2D pose to the test image. They lift the 2D pose to 3D using the method proposed in @cite_7 . @cite_9 propose to learn view-based silhouettes manifolds and the mapping function from the manifold to 3D poses. These approaches do not explicitly estimate camera parameters, but require a lot of training data from various viewpoints.
@cite_9 @cite_10 @cite_7
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We present a novel approach for detecting objects and estimating their 3D pose in single images of cluttered scenes. Objects are given in terms of 3D models without accompanying texture cues. A deformable parts-based model is trained on clusters of silhouettes of similar poses and produces hypotheses about possible object locations at test time. Objects are simultaneously segmented and verified inside each hypothesis bounding region by selecting the set of superpixels whose collective shape matches the model silhouette. A final iteration on the 6-DOF object pose minimizes the distance between the selected image contours and the actual projection of the 3D model. We demonstrate successful grasps using our detection and pose estimate with a PR2 robot. Extensive evaluation with a novel ground truth dataset shows the considerable benefit of using shape-driven cues for detecting objects in heavily cluttered scenes. This paper presents a 3D approach to multi-view object class detection. Most existing approaches recognize object classes for a particular viewpoint or combine classifiers for a few discrete views. We propose instead to build 3D representations of object classes which allow to handle viewpoint changes and intra-class variability. Our approach extracts a set of pose and class discriminant features from synthetic 3D object models using a filtering procedure, evaluates their suitability for matching to real image data and represents them by their appearance and 3D position. We term these representations 3D Feature Maps. For recognizing an object class in an image we match the synthetic descriptors to the real ones in a 3D voting scheme. Geometric coherence is reinforced by means of a robust pose estimation which yields a 3D bounding box in addition to the 2D localization. The precision of the 3D pose estimation is evaluated on a set of images of a calibrated scene. The 2D localization is evaluated on the PASCAL 2006 dataset for motorbikes and cars, showing that its performance can compete with state-of-the-art 2D object detectors. Estimating the precise pose of a 3D model in an image is challenging; explicitly identifying correspondences is difficult, particularly at smaller scales and in the presence of occlusion. Exemplar classifiers have demonstrated the potential of detection-based approaches to problems where precision is required. In particular, correlation filters explicitly suppress classifier response caused by slight shifts in the bounding box. This property makes them ideal exemplar classifiers for viewpoint discrimination, as small translational shifts can often be confounded with small rotational shifts. However, exemplar based pose-by-detection is not scalable because, as the desired precision of viewpoint estimation increases, the number of exemplars needed increases as well. We present a training framework to reduce an ensemble of exemplar correlation filters for viewpoint estimation by directly optimizing a discriminative objective. We show that the discriminatively reduced ensemble outperforms the state-of-the-art on three publicly available datasets and we introduce a new dataset for continuous car pose estimation in street scene images. The problem we consider in this paper is to take a single two-dimensional image containing a human figure, locate the joint positions, and use these to estimate the body configuration and pose in three-dimensional space. The basic approach is to store a number of exemplar 2D views of the human body in a variety of different configurations and viewpoints with respect to the camera. On each of these stored views, the locations of the body joints (left elbow, right knee, etc.) are manually marked and labeled for future use. The input image is then matched to each stored view, using the technique of shape context matching in conjunction with a kinematic chain-based deformation model. Assuming that there is a stored view sufficiently similar in configuration and pose, the correspondence process would succeed. The locations of the body joints are then transferred from the exemplar view to the test shape. Given the 2D joint locations, the 3D body configuration and pose are then estimated using an existing algorithm. We can apply this technique to video by treating each frame independently - tracking just becomes repeated recognition. We present results on a variety of data sets