Inferring 3D Shapes and Deformations from Single Views

By Yu Chen, Tae-Kyun Kim, Roberto Cipolla. 2010

80 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

End-to-End Recovery of Human Shape and Pose (2017, methodology): “Methods that output more than 3D joints: There are multiple methods that fit a parametric body model to manually extracted silhouettes [8] and a few manually provided correspondences [12, 14].”
Category-specific object reconstruction from a single image (2014, background): “Some interesting recent papers have extended variational approaches to handle categories [12, 13] but typically require some form of 3D annotations to bootstrap models.”
A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image (2016, background): “Methods such as [20], [21], [22], [23] fall into the category of nonlinear mapping, but like some of the aforementioned algorithms, they are limited to certain types of object categories, e.g., [3] is designed for reconstructing 3D human pose from 2D image landmarks and [4] focuses on 3D car…”
What Shape Are Dolphins? Building 3D Morphable Models from 2D Images (2013, unknown): “Chen et al. [13] learn separate pose and shape models for sharks as well as humans, and demonstrate good reconstructions by fitting these models to a single silhouette.”
3D Menagerie: Modeling the 3D Shape and Pose of Animals (2016, background): “Chen et al. [10] model both humans and sharks, factoring deformations into pose and shape.”
Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation (2016, unknown): “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”
Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image (2016, background): “Chen et al. [9] ﬁt a parametric model of body shape and pose to manually extracted silhouettes; they do not evaluate quantitative accuracy.”
Learning Category-Specific Deformable 3D Models for Object Reconstruction (2017, background): “approaches to handle categories [24], [51] but typically require some form of 3D annotations to bootstrap models.”
Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation (2016, unknown): “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”