Bayesian Image Based 3D Pose Estimation

By Marta Sanzari, Valsamis Ntouskos, Fiora Pirri. 2016

75 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

End-to-End Recovery of Human Shape and Pose (2017, methodology): “Two stage methods first predict 2D joint locations using 2D pose detectors [30, 49, 54] or ground truth 2D pose and then predict 3D joint locations from the 2D joints either by regression [26, 29] or model fitting, where a common approach exploits a learned dictionary of 3D skeletons [2, 34, 47, 37,”
LCR-Net++: Multi-Person 2D and 3D Pose Detection in Natural Images (2018, unknown): “[66] (17 jts) - 93.”
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image (2017, background): “[31], which it still outperforms on average across the entire dataset.”
Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation (2016, methodology): “3This it is not explicitly stated in [58], but the authors confirmed this to us by email.”
HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation (2020, methodology): “Two-stage approaches first estimate 2D pose and then lift them to 3D joint locations by a learned dictionary of 3D skeleton [1, 53, 64, 56, 76, 77] or regression [46, 71, 40, 11, 32, 59].”
LCR-Net: Localization-Classification-Regression for Human Pose (2017, methodology): “Our method is state of the art for 9 out of 15 actions but performs lower than [24] for 4 different actions.”
Self-Supervised Learning of 3D Human Pose Using Multi-View Geometry (2019, methodology): “Our self supervised (SS) model performs quite well compared to the recent fully 3D supervised methods [29, 32, 33, 40] which require abundant labeled data to learn.”
LCR-Net: Localization-Classiﬁcation-Regression for Human Pose (unknown): “Our method is state of the art for 9 out of 15 actions but performs lower than [24] for 4 different actions.”
3D Human Pose Estimation from a Single Image via Distance Matrix Regression (2016, unknown): “This protocol is used in [17, 23, 34, 46, 47, 56].”
Total Capture: 3D Human Pose Estimation Fusing Video and Inertial Sensors (2017, background): “Sanzari [26] estimates the location of 2D joints, before predicting 3D pose using appearance and probable 3-D pose of the discovered parts with a hierarchical Bayesian model.”