16 research outputs found
A closed-form solution to estimate uncertainty in non-rigid structure from motion
Semi-Definite Programming (SDP) with low-rank prior has been widely applied
in Non-Rigid Structure from Motion (NRSfM). Based on a low-rank constraint, it
avoids the inherent ambiguity of basis number selection in conventional
base-shape or base-trajectory methods. Despite the efficiency in deformable
shape reconstruction, it remains unclear how to assess the uncertainty of the
recovered shape from the SDP process. In this paper, we present a statistical
inference on the element-wise uncertainty quantification of the estimated
deforming 3D shape points in the case of the exact low-rank SDP problem. A
closed-form uncertainty quantification method is proposed and tested. Moreover,
we extend the exact low-rank uncertainty quantification to the approximate
low-rank scenario with a numerical optimal rank selection method, which enables
solving practical application in SDP based NRSfM scenario. The proposed method
provides an independent module to the SDP method and only requires the
statistic information of the input 2D tracked points. Extensive experiments
prove that the output 3D points have identical normal distribution to the 2D
trackings, the proposed method and quantify the uncertainty accurately, and
supports that it has desirable effects on routinely SDP low-rank based NRSfM
solver.Comment: 9 pages, 2 figure
Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation
We propose a novel method based on teacher-student learning framework for 3D
human pose estimation without any 3D annotation or side information. To solve
this unsupervised-learning problem, the teacher network adopts
pose-dictionary-based modeling for regularization to estimate a physically
plausible 3D pose. To handle the decomposition ambiguity in the teacher
network, we propose a cycle-consistent architecture promoting a 3D
rotation-invariant property to train the teacher network. To further improve
the estimation accuracy, the student network adopts a novel graph convolution
network for flexibility to directly estimate the 3D coordinates. Another
cycle-consistent architecture promoting 3D rotation-equivariant property is
adopted to exploit geometry consistency, together with knowledge distillation
from the teacher network to improve the pose estimation performance. We conduct
extensive experiments on Human3.6M and MPI-INF-3DHP. Our method reduces the 3D
joint prediction error by 11.4% compared to state-of-the-art unsupervised
methods and also outperforms many weakly-supervised methods that use side
information on Human3.6M. Code will be available at
https://github.com/sjtuxcx/ITES.Comment: Accepted in AAAI 202
MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views
We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from
Motion (NRSfM). MHR-Net aims to find a set of reasonable reconstructions for a
2D view, and it also selects the most likely reconstruction from the set. To
deal with the challenging unsupervised generation of non-rigid shapes, we
develop a new Deterministic Basis and Stochastic Deformation scheme in MHR-Net.
The non-rigid shape is first expressed as the sum of a coarse shape basis and a
flexible shape deformation, then multiple hypotheses are generated with
uncertainty modeling of the deformation part. MHR-Net is optimized with
reprojection loss on the basis and the best hypothesis. Furthermore, we design
a new Procrustean Residual Loss, which reduces the rigid rotations between
similar shapes and further improves the performance. Experiments show that
MHR-Net achieves state-of-the-art reconstruction accuracy on Human3.6M, SURREAL
and 300-VW datasets.Comment: Accepted to ECCV 202