Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images
By Matthias Dantone, Juergen Gall, Christian Leistner, Luc Van Gool. 2014
77 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Xiaogang Wang
- Cordelia Schmid
- Dacheng Tao
- Michael J. Black
- Bernt Schiele
- Ming-Hsuan Yang
In Human-Computer Interaction
- Michael J. Black
- Peter Gehler
- Bastian Leibe
- Martin Kiefel
- Ilya Kostrikov
- Umer Rafi
- Federica Bogo
- Juergen Gall
Citations in the corpus
- DensePose: Dense Human Pose Estimation in the Wild (2018, background): “Our approach can be understood as the next step in the line of works on human pose estimation [27, 1, 20, 7, 42, 19, 29].”
- Unite the People: Closing the Loop Between 3D and 2D Human Representations (2017, background): “The FashionPose dataset [9] would complement these well, but is annotated with a different set of keypoints: the neck joint is missing and the top head keypoint is replaced by the nose.”
- ClothCap (2017, methodology): “We will also investigate the use of Inertial Measurement Units on the body (von 1 Credits: First and third row images b) reproduced from Dantone et al. (2014), second row image b) reproduced from Johnson and Everingham (2010).”
- An Efficient Convolutional Network for Human Pose Estimation (2016, methodology): “The classical approaches are based on the pictorial structure model [1, 2, 7, 9, 13, 18, 19, 32] that uses a tree-structured graphical model to encode spatial dependencies between neighbouring joints.”
- MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation (2019, background): “Stacking several predictors sequentially has shown significant improvements in many tasks like human pose estimation [44], [45], [46].”
- Fashion Landmark Detection in the Wild (2016, background): “The second direction [11,12,31,32], on the other hand, focuses on the explicit modeling of landmark relationships using graphical models.”
- Multi-person Pose Estimation with Local Joint-to-Person Associations (2016, methodology): “For this, earlier approaches formulate the problem in a graphical model where interactions between body parts are modelled in a tree structure combined with local observations obtained from discriminatively trained part detectors [17–23].”