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The Space of Human Body Shapes: Reconstruction And Parameterization from Range Scans

by Brett Allen, Brian Curless, Zoran Popović - ACM TRANS. GRAPH , 2003
"... We develop a novel method for fitting high-resolution template meshes to detailed human body range scans with sparse 3D markers. We formulate an optimization problem in which the degrees of freedom are an affine transformation at each template vertex. The objective function is a weighted combination ..."
Abstract - Cited by 290 (4 self) - Add to MetaCart
We develop a novel method for fitting high-resolution template meshes to detailed human body range scans with sparse 3D markers. We formulate an optimization problem in which the degrees of freedom are an affine transformation at each template vertex. The objective function is a weighted

Acquiring linear subspaces for face recognition under variable lighting

by Kuang-chih Lee, Jeffrey Ho, David Kriegman - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2005
"... Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in ..."
Abstract - Cited by 317 (2 self) - Add to MetaCart
in one of three ways: A large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources, and again

Recovering 3D Human Pose from Monocular Images

by Ankur Agarwal, Bill Triggs
"... We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descrip ..."
Abstract - Cited by 261 (0 self) - Add to MetaCart
that the resulting system tracks long sequences stably, and is also capable of accurately reconstructing 3D human pose from single images, giving multiple possible solutions in ambiguous cases. For realism and good generalization over a wide range of viewpoints, we train the regressors on images resynthesized from

A Data-Driven Shape Model for Human Body Reconstruction from Photos

by Hyewon Seo
"... In this paper, we present a data-driven approach to the problem of reconstructing human body models from 2D images. One of the key tasks in reconstructing the 3D model from image data is shape recovery, a task done until now in utterly geometric way, in the domain of human body modeling. In contrast ..."
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In this paper, we present a data-driven approach to the problem of reconstructing human body models from 2D images. One of the key tasks in reconstructing the 3D model from image data is shape recovery, a task done until now in utterly geometric way, in the domain of human body modeling

Research Article Undersampled MR Image Reconstruction with Data-Driven Tight Frame

by Jianbo Liu, Shanshan Wang, Xi Peng, Dong Liang
"... which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing t ..."
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proposes a data-driven tight frame magnetic image reconstruction (DDTF-MRI) method. By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two-level Bregman iteration algorithm

Shape anchors for data-driven multi-view reconstruction

by Andrew Owens, Jianxiong Xiao, Antonio Torralba, William Freeman - In ICCV, 2013. 8
"... We present a data-driven method for building dense 3D reconstructions using a combination of recognition and multi-view cues. Our approach is based on the idea that there are image patches that are so distinctive that we can accurately estimate their latent 3D shapes solely using recognition. We cal ..."
Abstract - Cited by 2 (1 self) - Add to MetaCart
We present a data-driven method for building dense 3D reconstructions using a combination of recognition and multi-view cues. Our approach is based on the idea that there are image patches that are so distinctive that we can accurately estimate their latent 3D shapes solely using recognition. We

Bayesian reconstruction of 3d human motion from single-camera video

by Nicholas R. Howe, Michael E. Leventon, William T. Freeman - Advances in Neural Information Processing Systems , 1999
"... The three-dimensional motion of humans is underdetermined when the observation is limited to a single camera, due to the inherent 3D ambiguity of 2D video. We present a system that reconstructs the 3D motion of human subjects from single-camera video, relying on prior knowledge about human motion, l ..."
Abstract - Cited by 148 (3 self) - Add to MetaCart
The three-dimensional motion of humans is underdetermined when the observation is limited to a single camera, due to the inherent 3D ambiguity of 2D video. We present a system that reconstructs the 3D motion of human subjects from single-camera video, relying on prior knowledge about human motion

Practical aspects of a data-driven motion correction approach for brain SPECT

by Andre Z. Kyme, Brian F. Hutton, Senior Member, Rochelle L. Hatton, David W. Skerrett, Leighton R. Barnden - IEEE Trans. Med. Imaging, submitted , 2002
"... Abstract—Patient motion can cause image artifacts in single photon emission computed tomography despite restraining measures. Data-driven detection and correction of motion can be achieved by comparison of acquired data with the forward projections. This enables the brain locations to be estimated a ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
and data to be correctly incorporated in a three-dimensional (3-D) reconstruction algorithm. Digital and physical phantom experiments were performed to explore practical aspects of this approach. Methods: Noisy simulation data modeling multiple 3-D patient head movements were constructed by projecting

Inferring 3d body pose from silhouettes using activity manifold learning

by Ahmed Elgammal, Chan-su Lee - In CVPR , 2004
"... We aim to infer 3D body pose directly from human silhouettes. Given a visual input (silhouette), the objective is to recover the intrinsic body configuration, recover the view point, reconstruct the input and detect any spatial or temporal outliers. In order to recover intrinsic body configuration ( ..."
Abstract - Cited by 193 (12 self) - Add to MetaCart
We aim to infer 3D body pose directly from human silhouettes. Given a visual input (silhouette), the objective is to recover the intrinsic body configuration, recover the view point, reconstruct the input and detect any spatial or temporal outliers. In order to recover intrinsic body configuration

Data-Driven Shape Analysis and Processing

by Kai Xu, Vladimir G. Kim, Qixing Huang, Evangelos Kalogerakis , 2015
"... Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to impro ..."
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Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections
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