One of the widespread solutions for non-rigid tracking has a nested-loop
structure: with Gauss-Newton to minimize a tracking objective in the outer
loop, and Preconditioned Conjugate Gradient (PCG) to solve a sparse linear
system in the inner loop. In this paper, we employ learnable optimizations to
improve tracking robustness and speed up solver convergence. First, we upgrade
the tracking objective by integrating an alignment data term on deep features
which are learned end-to-end through CNN. The new tracking objective can
capture the global deformation which helps Gauss-Newton to jump over local
minimum, leading to robust tracking on large non-rigid motions. Second, we
bridge the gap between the preconditioning technique and learning method by
introducing a ConditionNet which is trained to generate a preconditioner such
that PCG can converge within a small number of steps. Experimental results
indicate that the proposed learning method converges faster than the original
PCG by a large margin.

通过内嵌学习算法的方式，对非刚性追踪问题进行优化，提高动态物体跟踪稳健性和求解器的收敛速度，实现了利用 CNN 学习特征进行全局形变的捕捉和快速生成预条件矩阵，在计算复杂度和实验结果方面胜过原算法。

学习优化非刚性跟踪

Learning to Optimize Non-Rigid Tracking

We present practical Levenberg-Marquardt variants of Gauss-Newton and natural
gradient methods for solving non-convex optimization problems that arise in
training deep neural networks involving enormous numbers of variables and huge
data sets. Our methods use subsampled Gauss-Newton or Fisher information
matrices and either subsampled gradient estimates (fully stochastic) or full
gradients (semi-stochastic), which, in the latter case, we prove convergent to
a stationary point. By using the Sherman-Morrison-Woodbury formula with
automatic differentiation (backpropagation) we show how our methods can be
implemented to perform efficiently. Finally, numerical results are presented to
demonstrate the effectiveness of our proposed methods.

使用 Levenberg-Marquardt 的 Gauss-Newton 和自然梯度方法，解决深度神经网络大量变量和海量数据集所产生的非凸优化问题，证明方法能够有效实现并提出数值结果。