  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.
