Bayesian Image Based 3D Pose Estimation

Sanzari, Marta; Ntouskos, Valsamis; Pirri, Fiora

doi:10.1007/978-3-319-46484-8_34

Cited by 54 publications

(61 citation statements)

References 40 publications

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“…This is reasonable for practical use under various scenarios. These methods are LinKDE [5], Tekin et al [15], Li et al [36], Zhou et al [14], Zhou et al [4], Du et al [37], Sanzari et al [34], Yasin et al [17], and Bogo et al [24]. Moreover, we compare other competing methods, i.e., Moreno-Noguer et al [38], Tome et al [20], Chen et al [16], Pavlakos et al [19], Zhou et al [23], Bruce et al [51], Tekin et al [50] and our conference version, i.e., Lin et al [21].…”

Section: Comparisons With Existing Methodsmentioning

confidence: 99%

“…Moreover, we compare other competing methods, i.e., Moreno-Noguer et al [38], Tome et al [20], Chen et al [16], Pavlakos et al [19], Zhou et al [23], Bruce et al [51], Tekin et al [50] and our conference version, i.e., Lin et al [21]. For those compared methods (i.e., [4], [5], [15], [16], [17], [24], [34], [36], [37], [38], [51]) whose source codes are not publicly available, we directly obtain their results from their published papers. For the other methods (i.e., [14], [19], [20], [21], [23], [50]), we directly use their official implementations for comparisons.…”

Section: Comparisons With Existing Methodsmentioning

confidence: 99%

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3D Human Pose Machines with Self-supervised Learning

Wang

Jiang

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

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Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests. In fact, completing this task is quite challenging due to the diverse appearances, viewpoints, occlusions and inherently geometric ambiguities inside monocular images. Most of the existing methods focus on designing some elaborate priors /constraints to directly regress 3D human poses based on the corresponding 2D human pose-aware features or 2D pose predictions. However, due to the insufficient 3D pose data for training and the domain gap between 2D space and 3D space, these methods have limited scalabilities for all practical scenarios (e.g., outdoor scene). Attempt to address this issue, this paper proposes a simple yet effective self-supervised correction mechanism to learn all intrinsic structures of human poses from abundant images. Specifically, the proposed mechanism involves two dual learning tasks, i.e., the 2D-to-3D pose transformation and 3D-to-2D pose projection, to serve as a bridge between 3D and 2D human poses in a type of "free" self-supervision for accurate 3D human pose estimation. The 2D-to-3D pose implies to sequentially regress intermediate 3D poses by transforming the pose representation from the 2D domain to the 3D domain under the sequence-dependent temporal context, while the 3D-to-2D pose projection contributes to refining the intermediate 3D poses by maintaining geometric consistency between the 2D projections of 3D poses and the estimated 2D poses. Therefore, these two dual learning tasks enable our model to adaptively learn from 3D human pose data and external large-scale 2D human pose data. We further apply our self-supervised correction mechanism to develop a 3D human pose machine, which jointly integrates the 2D spatial relationship, temporal smoothness of predictions and 3D geometric knowledge. Extensive evaluations on the Human3.6M and HumanEva-I benchmarks demonstrate the superior performance and efficiency of our framework over all the compared competing methods. Please find the code of this project at: http://www.sysu-hcp.net/3d pose ssl/ Index Terms-human pose estimation, convolutional neural networks, spatio-temporal modeling, self-supervised learning, geometric deep learning. ! K. Wang is with the

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Section: Comparisons With Existing Methodsmentioning

confidence: 99%

Section: Comparisons With Existing Methodsmentioning

confidence: 99%

3D Human Pose Machines with Self-supervised Learning

Wang

Jiang

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

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“…This paper is concerned with the commonly adopted pipeline in estimating 3D human pose from a single image [11], [18], [20], [21], which generally consists of two consecutive steps: 1) use 2D joints detectors to localize joints in 2D image space and 2) estimate 3D pose from these observations by regressors learned from motion capture datasets. However, there is an inherent issue with this pipeline when applying in the uncontrolled real world scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…1 for an example). This practical and challenging problem has not been properly addressed [18], [20] [11], [21].…”

Section: Introductionmentioning

confidence: 99%

Occluded Joints Recovery in 3D Human Pose Estimation based on Distance Matrix

Guo

Dai

2018

2018 24th International Conference on Pattern Recognition (ICPR)

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Albeit the recent progress in single image 3D human pose estimation due to the convolutional neural network, it is still challenging to handle real scenarios such as highly occluded scenes. In this paper, we propose to address the problem of single image 3D human pose estimation with occluded measurements by exploiting the Euclidean distance matrix (EDM). Specifically, we present two approaches based on EDM, which could effectively handle occluded joints in 2D images. The first approach is based on 2D-to-2D distance matrix regression achieved by a simple CNN architecture. The second approach is based on sparse coding along with a learned over-complete dictionary. Experiments on the Human3.6M dataset show the excellent performance of these two approaches in recovering occluded observations and demonstrate the improvements in accuracy for 3D human pose estimation with occluded joints. 1

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“…The steps of the proposed method are as follows. Given a video of a human activity both the 2D pose and 3D pose of the human are estimated (see [11], and also [12]). Once the 3D poses of the joints of interest are determined, we compute the motion flux.…”

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confidence: 99%

Discovery and recognition of motion primitives in human activities

2019

Self Cite

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We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the ‘motion flux’, a quantity which captures the motion variation of a group of skeletal joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate. The discovered primitives are unknown and unlabeled and are unsupervisedly collected into classes via a hierarchical non-parametric Bayes mixture model. Once classes are determined and labeled they are further analyzed for establishing models for recognizing discovered primitives. Each primitive model is defined by a set of learned parameters. Given new video data and given the estimated pose of the subject appearing on the video, the motion is segmented into primitives, which are recognized with a probability given according to the parameters of the learned models. Using our framework we build a publicly available dataset of human motion primitives, using sequences taken from well-known motion capture datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields including video analysis, human inspired motion generation, learning by demonstration, intuitive human-robot interaction, and human behavior analysis.

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Bayesian Image Based 3D Pose Estimation

Cited by 54 publications

References 40 publications

3D Human Pose Machines with Self-supervised Learning

3D Human Pose Machines with Self-supervised Learning

Occluded Joints Recovery in 3D Human Pose Estimation based on Distance Matrix

Discovery and recognition of motion primitives in human activities

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