Human Pose Estimation Using Body Parts Dependent Joint Regressors

By Matthias Dantone, Juergen Gall, Christian Leistner, Luc Van Gool. 2013

305 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, 3D Vision & Pose, Human-Computer Interaction

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Citations in the corpus

Towards Accurate Multi-person Pose Estimation in the Wild (2017, neutral): “D human pose inference [3, 12, 37, 45, 11, 27, 32, 38, 18].”
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields (2016, background): “…models [10, 30, 4, 3, 26, 40, 17], which parametrically encode the spatial relationship between adjacent parts following a kinematic chain, or non-tree models [37, 31, 20, 18, 8] that augment the tree structure with additional edges to capture occlusion, symmetry, and long-range relationships.”
DeepPose: Human Pose Estimation via Deep Neural Networks (2013, background): “[2] over an extended range of distances to true joint: [0, 0.”
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation (2014, background): “Subsequently a large number of related models were developed [2, 11, 37, 10].”
2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning (2018, background): “The problem of human pose estimation has been intensively studied in the last years, from Pictorial Structures [2, 17, 37] to more recent CNN approaches [34, 25, 38, 20, 41, 54, 5, 51, 52, 36].”
Efficient object localization using Convolutional Networks (2014, methodology): “Following the seminal work of Felzenszwalb et al. [10] on ‘Deformable Part Models’ (DPM) for human-body-pose estimation, many algorithms have been proposed to improve on the DPM archiecture [2, 9, 23, 7].”
Pose Machines: Articulated Pose Estimation via Inference Machines (2014, background): “In [21] better part detectors are learned by using multiple stages of random forests.”
Monocular human pose estimation: A survey of deep learning-based methods (2020, methodology): “…deep learning brings a huge impact on vision-based human pose estimation, traditional 2D HPE algorithms adopt hand-craft feature extraction and sophisticated body models to obtain local representations and global pose structures (Dantone et al., 2013; Chen and Yuille, 2014; Gkioxari et al., 2014b).”
CrowdPose: Efficient Crowded Scenes Pose Estimation and a New Benchmark (2018, background): “Pioneer works on 2D human pose estimation dataset on RGB images involve LSP [17], FashionPose [9], PASCAL Person Layout [10], J-HMDB [16], etc.”
Deep Learning-based Human Pose Estimation: A Survey (2020, unknown): “Traditional 2D HPE methods adopt different hand-crafted feature extraction techniques [33] [34] for body parts, and these early works describe human body as a stick figure to obtain global pose structures.”
Fine-Grained Activity Recognition with Holistic and Pose Based Features (2014, background): “However, articulated pose estimation is a challenging and non-trivial task that is subject of ongoing research [18,19,32,8,24].”
Towards Viewpoint Invariant 3D Human Pose Estimation (2016, background): “There has been a long line of work on generative pose models, where a pose is estimated by constructing a skeleton using templates or priors in a top-down manner [19,12,16,18].”
An Efficient Convolutional Network for Human Pose Estimation (2016, methodology): “The classical approaches are based on the pictorial structures model [1, 2, 7, 10, 14, 18, 19, 32] that uses a tree-structured graphical model to encode spatial dependencies between neighbouring joints.”
Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark (2018, background): “These small datasets are unsuitable for training models with complex appearance representations and multiple components [31], [37], [38], which could perform better.”
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation (2015, background): “The idea of probabilistic voting was also exploited in object tracking [18], action recognition [28], facial feature detection [12], and human pose estimation [13].”
Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation (2014, methodology): “Body labeling methods have also been proposed in the context of person detection[4], to learn a model of the body[13] and recently for 2D pose prediction[21, 11], but not within models and algorithms carrying all the way to 3D pose estimation.”
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation (2014, methodology): “Many algorithms that use DPM for creating the body part unary distribution [36, 6, 7, 37] with spatial-models incorporating body-part relationship priors have since then been developed.”
Combining local appearance and holistic view: Dual-Source Deep Neural Networks for human pose estimation (2015, methodology): “Following the pictorial-structure model [19, 1], a variety of part-based methods have been developed for human pose estimation [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15].”