A Survey of Efficient Regression of General-Activity Human Poses from Depth Images <s> Related Works <s> This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in our work, the tight coupling between those two processes allows them to benefit from each other and improve the combined performance. ::: ::: The core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is employed in an MDL based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion. ::: ::: An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems. <s> BIB001 </s> A Survey of Efficient Regression of General-Activity Human Poses from Depth Images <s> Related Works <s> This paper addresses human pose recognition from video sequences by formulating it as a classification problem. Unlike much previous work we do not make any assumptions on the availability of clean segmentation. The first step of this work consists in a novel method of aligning the training images using 3D Mocap data. Next we define classes by discretizing a 2D manifold whose two dimensions are camera viewpoint and actions. Our main contribution is a pose detection algorithm based on random forests. A bottom-up approach is followed to build a decision tree by recursively clustering and merging the classes at each level. For each node of the decision tree we build a list of potentially discriminative features using the alignment of training images; in this paper we consider Histograms of Orientated Gradient (HOG). We finally grow an ensemble of trees by randomly sampling one of the selected HOG blocks at each node. Our proposed approach gives promising results with both fixed and moving cameras. <s> BIB002 </s> A Survey of Efficient Regression of General-Activity Human Poses from Depth Images <s> Related Works <s> We address the classic problems of detection, segmentation and pose estimation of people in images with a novel definition of a part, a poselet. We postulate two criteria (1) It should be easy to find a poselet given an input image (2) it should be easy to localize the 3D configuration of the person conditioned on the detection of a poselet. To permit this we have built a new dataset, H3D, of annotations of humans in 2D photographs with 3D joint information, inferred using anthropometric constraints. This enables us to implement a data-driven search procedure for finding poselets that are tightly clustered in both 3D joint configuration space as well as 2D image appearance. The algorithm discovers poselets that correspond to frontal and profile faces, pedestrians, head and shoulder views, among others. Each poselet provides examples for training a linear SVM classifier which can then be run over the image in a multiscale scanning mode. The outputs of these poselet detectors can be thought of as an intermediate layer of nodes, on top of which one can run a second layer of classification or regression. We show how this permits detection and localization of torsos or keypoints such as left shoulder, nose, etc. Experimental results show that we obtain state of the art performance on people detection in the PASCAL VOC 2007 challenge, among other datasets. We are making publicly available both the H3D dataset as well as the poselet parameters for use by other researchers. <s> BIB003 </s> A Survey of Efficient Regression of General-Activity Human Poses from Depth Images <s> Related Works <s> We deal with the problem of detecting and identifying body parts in depth images at video frame rates. Our solution involves a novel interest point detector for mesh and range data that is particularly well suited for analyzing human shape. The interest points, which are based on identifying geodesic extrema on the surface mesh, coincide with salient points of the body, which can be classified as, e.g., hand, foot or head using local shape descriptors. Our approach also provides a natural way of estimating a 3D orientation vector for a given interest point. This can be used to normalize the local shape descriptors to simplify the classification problem as well as to directly estimate the orientation of body parts in space. Experiments involving ground truth labels acquired via an active motion capture system show that our interest points in conjunction with a boosted patch classifier are significantly better in detecting body parts in depth images than state-of-the-art sliding-window based detectors. <s> BIB004 </s> A Survey of Efficient Regression of General-Activity Human Poses from Depth Images <s> Related Works <s> In this paper, we propose a system, that automatically transfers human body motion captured from an ordinary video camera to an unknown 3D character mesh. In our system, no manual intervention is required for specifying the internal skeletal structure or defining how the mesh surfaces deform. A sparse graph is generated from the input polygons based on their connectivity and geometric distributions. To estimate articulated body parts in the video, a progressive particle filter is used for identifying correspondences. We anticipate our proposed system can bring animation to a new audience with a more intuitive user interface. <s> BIB005 </s> A Survey of Efficient Regression of General-Activity Human Poses from Depth Images <s> Related Works <s> 3D character with human motion offers a high end technology for creating compelling contents in graphics. In this paper, we present an automatic system to animate a 3D character with human motion streamed from a single video camera. The traditional mesh animation process is laborious and requires high skills from the users. To mitigate this limitation, a new way for bringing 3D objects to life is introduced that does not need explicit mesh positioning. In our framework, the animation is driven by the captured motion from an ordinary RGB camera. In order to reduce the ambiguity of the estimated 3D pose, a modified spatio-temporal constraint based algorithm is used for articulated gesture estimation across frames while maintaining temporal coherence. Our approach demonstrates promising performance on par with state-of-the-art techniques. We believe the presented animation system will allow a new audience of novice users to easily and efficiently create animation for arbitrary 3D characters. <s> BIB006
In previous works, one common idea in human pose estimation is to focus on finding different body parts. Bourdev et al. BIB003 put foward a two-layer regression model that trains segments classifers to local patterns detection and combines the output of the classifiers. Plagemann et al. BIB004 create a novel interest point detector for catching body components from depth images. Shotton et al.  design a system that change an single input depth image to an inferred per-pixel body part distribution and local the 3D joint position. Pictorial structures based methods have also been used for human pose estimation to enhance the body shape BIB005 . This method can optimally remove the ambiguity of 3D inference from a single view point. Pictorial structures can also be used together with segmentation to efficiently localize body part and predict the joint position BIB006 . However, this idea has some problems with the consideration of the required definition of body alignment, joints inside the body, and body occlusion. The implicit shape model BIB001 can solve these problems. Random forest BIB002 based method also have advantages over the body-part-based methods.