Detailed Human Shape and Pose from Images

By Alexandru O. Balan, Leonid Sigal, Michael J. Black, James Davis, Horst W. Haussecker. 2007

363 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

Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “Among a large number of human representation approaches [24], [25], [26], [27], [28], [29], most of the existing 3D based methods can be broadly grouped into two categories: representations based on local features [30], [31] and skeletonbased representations [32] [33] [34].”
Articulated mesh animation from multi-view silhouettes (2008, background): “Articulated templates are often used to improve pose tracking, but they do not deform to capture details in the video [Carranza et al. 2003; Theobalt et al. 2007; Cheung et al. 2005; Anguelov et al. 2005; Corazza et al. 2006; Balan et al. 2007].”
Multilinear pose and body shape estimation of dressed subjects from image sets (2010, methodology): “B˘ et al. use the SCAPE model to estimate pose and shape from multiview video streams [ 6 ].”
ARCH: Animatable Reconstruction of Clothed Humans (2020, background): “There are multiple attempts to solve this task with video inputs [2, 3, 37, 1, 52], RGB-D data [53, 56] and in multi-view settings [5, 13, 14, 45, 46, 47, 48, 6].”
Self-supervised Learning of Motion Capture (2017, methodology): “Numerous 3D shape reconstruction methods have used such segmentation consistency constraint [36; 2; 4], but again, in an optimization as opposed to learning framework.”
VNect (2017, neutral): “CCS Concepts: • Computing methodologies → Reconstruction ; Computational photography ; Image-based rendering ;”
HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion (2010, background): “We make no attempts to fit the shape of the limbs to the image measurements (Balan et al. 2007).”
Home 3D body scans from noisy image and range data (2011, methodology): “Consequently, many methods that use distance maps either use uni-directional distance, from model silhouette to static observed silhouette [20, 24] or use a derivative-free optimizer [5].”
3D Human Pose Estimation from a Single Image via Distance Matrix Regression (2016, background): “The most straightforward generative model consists in representing human pose as linear combinations of modes learned from training data [5].”
Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation (2016, neutral): “In the past, this usually involved inferring a 3D human pose by optimizing an energy function derived from image information, such as silhouettes [6, 14, 21, 22, 25, 31, 44, 49, 60], trajectories [75], cm im”
Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation (2016, neutral): “In the past, this usually involved inferring a 3D human pose either optimizing an energy function derived from image information, such as silhouettes [5, 13, 20, 21, 24, 29, 43, 53], feature trajectories [68] and 2D joint locations [3, 4, 19, 33, 45, 51, 62, 63], or 2D recognitionbased pose retrieva”
Inverse Dynamics for Action Recognition (2013, background): “This is a realistic assumption, as current markerless tracking algorithms can provide accurate tracking results [17], [19], [48], [49].”
A Nonrigid Kernel-Based Framework for 2D-3D Pose Estimation and 2D Image Segmentation (2011, neutral): “In relation to this work, our focus would be to accurately quantify the deformation through a set of 3D models like that of [1], through kernel principal component analysis (KPCA).”
Accurate 3D pose estimation from a single depth image (2011, background): “, [4, 3]) generate high quality human shape models with pose information.”
BodyNet: Volumetric Inference of 3D Human Body Shapes (2018, neutral): “Earlier work [39,40] proposed to optimize pose and shape parameters of the 3D deformable body model SCAPE [41].”