Articulated people detection and pose estimation: Reshaping the future

By Leonid Pishchulin, Arjun Jain, Mykhaylo Andriluka, Thorsten Thormählen, Bernt Schiele. 2012

258 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Towards Accurate Multi-person Pose Estimation in the Wild (2017, methodology): “For example, Pishchulin et al. [34] follow this paradigm by using PS-based pose estimation.”
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields (2016, methodology): “A common approach [28, 11, 33, 16] is to divide and conquer the problem by employing a person detector and performing single-person pose estimation for each detection.”
DensePose: Dense Human Pose Estimation in the Wild (2018, background): “Synthetic ground-truth can be established by rendering images using surface-based models [33, 32, 37, 11, 5, 30].”
Learning from Simulated and Unsupervised Images through Adversarial Training (2016, background): “various prediction tasks, including gaze estimation [43], text detection and classification in RGB images [9, 15], font recognition [42], object detection [10, 27], hand pose estimation in depth images [38, 37], scene recognition in RGB-D [11], semantic segmentation of urban scenes [31], and human p”
Recurrent Human Pose Estimation (2016, methodology): “many years approaches have used variations on the pictorial structure model [14], [16] of a combination of part detectors and configuration constraints [2], [15], [30], [34], [44].”
Strong Appearance and Expressive Spatial Models for Human Pose Estimation (2013, methodology): “Part detectors are either trained independently [17, 22] or jointly with the rest of the model [34, 7].”
Monocular 3D Human Pose Estimation Using Transfer Learning and Improved CNN Supervision (2016, background): “warp human size in images with a parametric body model[52].”
Human Pose Estimation Using Body Parts Dependent Joint Regressors (2013, methodology): “Furthermore, human body models have been used to obtain better shapes of the body parts [42] or to synthesize training data [23].”
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-pixel Ground Truth Using Stochastic Grammars (2017, background): “2015), view-point estimation (Movshovitz-Attias et al. 2014; Su et al. 2015), 2D human pose estimation (Pishchulin et al. 2012; Romero et al. 2015; Qiu 2016), 3D human pose estimation (Shotton et al. 2013; Shakhnarovich et al. 2003; Yasin et al. 2016; Du et al. 2016; Ghezelghieh et al. 2016; Rogez…”
Monocular 3D Human Pose Estimation in the Wild Using Improved CNN Supervision (2016, background): “warp human size in images with a parametric body model [42].”
Benchmarking and Error Diagnosis in Multi-instance Pose Estimation (2017, methodology): “…the performance of human pose estimation algorithms has recently improved dramatically, thanks to the development of suitable deep architectures [9, 11, 12, 17, 28, 29, 32, 33, 42, 44] and the availability of well-annotated image datasets, such as MPII Human Pose Dataset and COCO [4, 27].”
Multi-task Recurrent Neural Network for Immediacy Prediction (2015, methodology): “Both holistic models [28, 11, 19, 20, 33] and local models [31, 8, 39, 6, 9, 30, 24, 10, 26, 14, 29, 35, 25, 1, 21, 4] were used for estimating the pose of a single person.”
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time (2022, methodology): “Ourwork follows the top-down paradigm like others[9], [20], [28], [29], [30], [31], which first obtains the bounding box for each human body through object detector and then performs single-person pose estimation on Fig.”
MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild (2016, background): “Later, [25] augmented labelled training images with small perturbations in a similar way.”
What Is Holding Back Convnets for Detection? (2015, background): “Some of the strategies considered include video game renderings [39] (aiming at photo-realism), CAD model wire-frame renderings [32,24] (focusing on object boundaries), or augmenting the training set by subtle deformations of the positive samples [7,25].”
Articulated pose estimation with tiny synthetic videos (2015, background): “Unlike other synthesis-based approaches which produce highquality synthetic images ([24], [13]) our goal is to produce a large set of reasonably photorealistic images which captures most of the variability expected in the future frames.”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, methodology): “Traditionally, a two-staged approach was used by first detecting body parts and then estimating the pose as in (Pishchulin et al. (2012); Gkioxari et al. (2014); Sun & Savarese (2011)).”