Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> Recovering the 3D coordinates of various joints of the human body from an image is a critical first step for several model-based human tracking and optical motion capture systems. Unlike previous approaches that have used a restrictive camera model or assumed a calibrated camera, our work deals with the general case of a perspective uncalibrated camera and is thus well suited for archived video. The input to the system is an image of the human body and correspondences of several body landmarks, while the output is the set of 3D coordinates of the landmarks in a body-centric coordinate system. Using ideas from 3D model based invariants, we set up a polynomial system of equations in the unknown head pitch, yaw and roll angles. If we are able to make the often-valid assumption that the torso twist is small, there are finite numbers of solutions to the head-orientation that can be computed readily. Once the head orientation is computed, the epipolar geometry of the camera is recovered, leading to solutions to the 3D joint positions. Results are presented on synthetic and real images. <s> BIB001 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> This paper demonstrates a new visual motion estimation technique that is able to recover high degree-of-freedom articulated human body configurations in complex video sequences. We introduce the use and integration of a mathematical technique, the product of exponential maps and twist motions, into a differential motion estimation. This results in solving simple linear systems, and enables us to recover robustly the kinematic degrees-of-freedom in noise and complex self occluded configurations. A new factorization technique lets us also recover the kinematic chain model itself. We are able to track several human walk cycles, several wallaby hop cycles, and two walk cycels of the famous movements of Eadweard Muybridge's motion studies from the last century. To the best of our knowledge, this is the first computer vision based system that is able to process such challenging footage. <s> BIB002 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> We develop a modified particle filter which is shown to be effective at searching the high-dimensional configuration spaces (c. 30 + dimensions) encountered in visual tracking of articulated body motion. The algorithm uses a continuation principle, based on annealing, to introduce the influence of narrow peaks in the fitness function, gradually. The new algorithm, termed annealed particle filtering, is shown to be capable of recovering full articulated body motion efficiently. A mechanism for achieving a soft partitioning of the search space is described and implemented, and shown to improve the algorithm’s performance. Likewise, the introduction of a crossover operator is shown to improve the effectiveness of the search for kinematic trees (such as a human body). Results are given for a variety of agile motions such as walking, running and jumping. <s> BIB003 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> We describe a mixture density propagation algorithm to estimate 3D human motion in monocular video sequences based on observations encoding the appearance of image silhouettes. Our approach is discriminative rather than generative, therefore it does not require the probabilistic inversion of a predictive observation model. Instead, it uses a large human motion capture data-base and a 3D computer graphics human model in order to synthesize training pairs of typical human configurations together with their realistically rendered 2D silhouettes. These are used to directly learn to predict the conditional state distributions required for 3D body pose tracking and thus avoid using the generative 3D model for inference (the learned discriminative predictors can also be used, complementary, as importance samplers in order to improve mixing or initialize generative inference algorithms). We aim for probabilistically motivated tracking algorithms and for models that can represent complex multivalued mappings common in inverse, uncertain perception inferences. Our paper has three contributions: (1) we establish the density propagation rules for discriminative inference in continuous, temporal chain models; (2) we propose flexible algorithms for learning multimodal state distributions based on compact, conditional Bayesian mixture of experts models; and (3) we demonstrate the algorithms empirically on real and motion capture-based test sequences and compare against nearest-neighbor and regression methods. <s> BIB004 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> This paper presents a model-based, Cartesian control theoretic approach for estimating human pose from features detected using depth images obtained from a time of flight imaging device. The features represent positions of anatomical landmarks, detected and tracked over time based on a probabilistic inferencing algorithm. The detected features are subsequently used as input to a constrained, closed loop tracking control algorithm which not only estimates the pose of the articulated human model, but also provides feedback to the feature detector in order to resolve ambiguities or to provide estimates of undetected features. Based on a simple kinematic model, constraints such as joint limit avoidance, and self penetration avoidance are enforced within the tracking control framework. We demonstrate the effectiveness of the algorithm with experimental results of upper body pose reconstruction from a small set of features. On average, the entire pipeline runs at approximately 10 frames per second on a standard 3 GHz PC using a 17 degree of freedom upper body human model. <s> BIB005 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> Local optimization and filtering have been widely applied to model-based 3D human motion capture. Global stochastic optimization has recently been proposed as promising alternative solution for tracking and initialization. In order to benefit from optimization and filtering, we introduce a multi-layer framework that combines stochastic optimization, filtering, and local optimization. While the first layer relies on interacting simulated annealing and some weak prior information on physical constraints, the second layer refines the estimates by filtering and local optimization such that the accuracy is increased and ambiguities are resolved over time without imposing restrictions on the dynamics. In our experimental evaluation, we demonstrate the significant improvements of the multi-layer framework and provide quantitative 3D pose tracking results for the complete HumanEva-II dataset. The paper further comprises a comparison of global stochastic optimization with particle filtering, annealed particle filtering, and local optimization. <s> BIB006 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> We introduce a physics-based model for 3D person tracking. Based on a biomechanical characterization of lower-body dynamics, the model captures important physical properties of bipedal locomotion such as balance and ground contact. The model generalizes naturally to variations in style due to changes in speed, step-length, and mass, and avoids common problems (such as footskate) that arise with existing trackers. The dynamics comprise a two degree-of-freedom representation of human locomotion with inelastic ground contact. A stochastic controller generates impulsive forces during the toe-off stage of walking, and spring-like forces between the legs. A higher-dimensional kinematic body model is conditioned on the underlying dynamics. The combined model is used to track walking people in video, including examples with turning, occlusion, and varying gait. We also report quantitative monocular and binocular tracking results with the HumanEva dataset. <s> BIB007 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> Markerless tracking of human pose is a hard yet relevant problem. In this paper, we derive an efficient filtering algorithm for tracking human pose using a stream of monocular depth images. The key idea is to combine an accurate generative model — which is achievable in this setting using programmable graphics hardware — with a discriminative model that provides data-driven evidence about body part locations. In each filter iteration, we apply a form of local model-based search that exploits the nature of the kinematic chain. As fast movements and occlusion can disrupt the local search, we utilize a set of discriminatively trained patch classifiers to detect body parts. We describe a novel algorithm for propagating this noisy evidence about body part locations up the kinematic chain using the un-scented transform. The resulting distribution of body configurations allows us to reinitialize the model-based search. We provide extensive experimental results on 28 real-world sequences using automatic ground-truth annotations from a commercial motion capture system. <s> BIB008 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> In recent years, depth cameras have become a widely available sensor type that captures depth images at real-time frame rates. Even though recent approaches have shown that 3D pose estimation from monocular 2.5D depth images has become feasible, there are still challenging problems due to strong noise in the depth data and self-occlusions in the motions being captured. In this paper, we present an efficient and robust pose estimation framework for tracking full-body motions from a single depth image stream. Following a data-driven hybrid strategy that combines local optimization with global retrieval techniques, we contribute several technical improvements that lead to speed-ups of an order of magnitude compared to previous approaches. In particular, we introduce a variant of Dijkstra's algorithm to efficiently extract pose features from the depth data and describe a novel late-fusion scheme based on an efficiently computable sparse Hausdorff distance to combine local and global pose estimates. Our experiments show that the combination of these techniques facilitates real-time tracking with stable results even for fast and complex motions, making it applicable to a wide range of inter-active scenarios. <s> BIB009 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> Model-based pose estimation algorithms aim at recovering human motion from one or more camera views and a 3D model representation of the human body. The model pose is usually parameterized with a kinematic chain and thereby the pose is represented by a vector of joint angles. The majority of algorithms are based on minimizing an error function that measures how well the 3D model fits the image. This category of algorithms usually has two main stages, namely defining the model and fitting the model to image observations. In the first section, the reader is introduced to the different kinematic parametrization of human motion. In the second section, the most commonly used representations of the human shape are described. The third section is dedicated to the description of different error functions proposed in the literature and to common optimization techniques used for human pose estimation. Specifically, local optimization and particle-based optimization and filtering are discussed and compared. The chapter concludes with a discussion of the state-of-the-art in model-based pose estimation, current limitations and future directions. <s> BIB010 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> We present an approach for modeling the human body by Sums of spatial Gaussians (SoG), allowing us to perform fast and high-quality markerless motion capture from multi-view video sequences. The SoG model is equipped with a color model to represent the shape and appearance of the human and can be reconstructed from a sparse set of images. Similar to the human body, we also represent the image domain as SoG that models color consistent image blobs. Based on the SoG models of the image and the human body, we introduce a novel continuous and differentiable model-to-image similarity measure that can be used to estimate the skeletal motion of a human at 5–15 frames per second even for many camera views. In our experiments, we show that our method, which does not rely on silhouettes or training data, offers an good balance between accuracy and computational cost. <s> BIB011 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> Human motion capturing (HMC) from multiview image sequences is an extremely difficult problem due to depth and orientation ambiguities and the high dimensionality of the state space. In this paper, we introduce a novel hybrid HMC system that combines video input with sparse inertial sensor input. Employing an annealing particle-based optimization scheme, our idea is to use orientation cues derived from the inertial input to sample particles from the manifold of valid poses. Then, visual cues derived from the video input are used to weight these particles and to iteratively derive the final pose. As our main contribution, we propose an efficient sampling procedure where the particles are derived analytically using inverse kinematics on the orientation cues. Additionally, we introduce a novel sensor noise model to account for uncertainties based on the von Mises-Fisher distribution. Doing so, orientation constraints are naturally fulfilled and the number of needed particles can be kept very small. More generally, our method can be used to sample poses that fulfill arbitrary orientation or positional kinematic constraints. In the experiments, we show that our system can track even highly dynamic motions in an outdoor environment with changing illumination, background clutter, and shadows. <s> BIB012 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> Fitting an articulated model to image data is often approached as an optimization over both model pose and model-to-image correspondence. For complex models such as humans, previous work has required a good initialization, or an alternating minimization between correspondence and pose. In this paper we investigate one-shot pose estimation: can we directly infer correspondences using a regression function trained to be invariant to body size and shape, and then optimize the model pose just once? We evaluate on several challenging single-frame data sets containing a wide variety of body poses, shapes, torso rotations, and image cropping. Our experiments demonstrate that one-shot pose estimation achieves state of the art results and runs in real-time. <s> BIB013 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> Tracking human pose in real-time is a difficult problem with many interesting applications. Existing solutions suffer from a variety of problems, especially when confronted with unusual human poses. In this paper, we derive an algorithm for tracking human pose in real-time from depth sequences based on MAP inference in a probabilistic temporal model. The key idea is to extend the iterative closest points (ICP) objective by modeling the constraint that the observed subject cannot enter free space, the area of space in front of the true range measurements. Our primary contribution is an extension to the articulated ICP algorithm that can efficiently enforce this constraint. The resulting filter runs at 125 frames per second using a single desktop CPU core. We provide extensive experimental results on challenging real-world data, which show that the algorithm outperforms the previous state-of-the-art trackers both in computational efficiency and accuracy. <s> BIB014 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> We propose a framework to reconstruct the 3D pose of a human for animation from a sequence of single-view video frames. The framework for pose construction starts with background estimation and the performer@?s silhouette is extracted using image subtraction for each frame. Then the body silhouettes are automatically labeled using a model-based approach. Finally, the 3D pose is constructed from the labeled human silhouette by assuming orthographic projection. The proposed approach does not require camera calibration. It assumes that the input video has a static background, it has no significant perspective effects, and the performer is in an upright position. The proposed approach requires minimal user interaction. <s> BIB015 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> Recent works on 3D human pose tracking using unsupervised methods typically focus on improving the optimization framework to find a better maximum in the likelihood function (i.e., the tracker). In contrast, in this paper, we focus on improving the likelihood function, by making it more robust and less ambiguous, thus making the optimization task easier. In particular, we propose an exponential chamfer distance for model matching that is robust to small pose changes, and a part-based model that is better able to localize partially occluded and overlapping parts. Using a standard annealing particle filter and simple diffusion motion model, the proposed likelihood function obtains significantly lower error than other unsupervised tracking methods on the HumanEva dataset. Noting that the joint system of the tracker’s body model is different than the joint system of the motion capture ground-truth model, we propose a novel method for transforming between the two joint systems. Applying this bias correction, our part-based likelihood obtains results equivalent to state-of-the-art supervised tracking methods. <s> BIB016 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Generative Methods <s> A Bayesian framework for 3-D human pose estimation from monocular images based on sparse representation (SR) is introduced. Our probabilistic approach aims at simultaneously learning two overcomplete dictionaries (one for the visual input space and the other for the pose space) with a shared sparse representation. Existing SR-based pose estimation approaches only offer a point estimation of the dictionary and the sparse codes. Therefore, they might be unreliable when the number of training examples is small. Our Bayesian framework estimates a posterior distribution for the sparse codes and the dictionaries from labeled training data. Hence, it is robust to overfitting on small-size training data. Experimental results on various human activities show that the proposed method is superior to the state-of-the-art pose estimation algorithms. <s> BIB017
The predictions made at the pixel level yield a set of independent local pose cues that are unlikely to respect kinematic constraints. By fitting a generative model to these cues, BIB013 BIB009 BIB008 resolve this problem. Generative approaches  BIB001 BIB010 BIB015 BIB016 BIB017 BIB005 model the likelihood of the observations given a pose estimate. Inference involves a complex search over the state space to locate the peaks of the likelihood BIB004 . Generative methods are susceptible to local minima, and thus require good initial pose estimates, regardless of the optimization scheme used. The pose is typically inferred using local optimization BIB002 BIB007 BIB014  BIB011 or stochastic search BIB003 BIB006 BIB012 .