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Found 8 papers, 1 papers with code
Fusing Monocular Images and Sparse IMU Signals for Real-time Human Motion Capture
We believe that the combination is complementary and able to solve the inherent difficulties of using one modality input, including occlusions, extreme lighting/texture, and out-of-view for visual mocap and global drifts for inertial mocap.
Ranked #1 on
3D Human Pose Estimation
on AIST++
EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors
We integrate the two techniques together in EgoLocate, a system that simultaneously performs human motion capture (mocap), localization, and mapping in real time from sparse body-mounted sensors, including 6 inertial measurement units (IMUs) and a monocular phone camera.
An inexact LPA for DC composite optimization and application to matrix completions with outliers
For this class of nonconvex and nonsmooth problems, we propose an inexact linearized proximal algorithm (iLPA) by computing in each step an inexact minimizer of a strongly convex majorization constructed with a partial linearization of their objective functions at the current iterate, and establish the convergence of the generated iterate sequence under the Kurdyka-\L\"ojasiewicz (KL) property of a potential function.
Column $\ell_{2,0}$-norm regularized factorization model of low-rank matrix recovery and its computation
This paper is concerned with the column $\ell_{2, 0}$-regularized factorization model of low-rank matrix recovery problems and its computation.
Error bound of critical points and KL property of exponent $1/2$ for squared F-norm regularized factorization
This paper is concerned with the squared F(robenius)-norm regularized factorization form for noisy low-rank matrix recovery problems.
KL property of exponent $1/2$ of $\ell_{2,0}$-norm and DC regularized factorizations for low-rank matrix recovery
To cater for the scenario in which only a coarse estimation is available for the rank of the true matrix, an $\ell_{2, 0}$-norm regularized term is added to the factored loss function to reduce the rank adaptively; and account for the ambiguities in the factorization, a balanced term is then introduced.
Equivalent Lipschitz surrogates for zero-norm and rank optimization problems
Specifically, we reformulate these combinatorial problems as equivalent MPECs by the variational characterization of the zero-norm and rank function, show that their penalized problems, yielded by moving the equilibrium constraint into the objective, are the global exact penalization, and obtain the equivalent Lipschitz surrogates by eliminating the dual variable in the global exact penalty.
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients
To seek a solution of high recovery quality beyond the reach of the nuclear norm, in this paper, we propose a rank-corrected procedure using a nuclear semi-norm to generate a new estimator.
