  Tracking the 3D shape of a deforming object using only monocular 2D vision is
a challenging problem. This is because one should (i) infer the 3D shape from a
2D image, which is a severely underconstrained problem, and (ii) implement the
whole solution pipeline in real-time. The pipeline typically requires feature
detection and matching, mismatch filtering, 3D shape inference and feature
tracking algorithms. We propose ROBUSfT, a conventional pipeline based on a
template containing the object's rest shape, texturemap and deformation law.
ROBUSfT is ready-to-use, wide-baseline, capable of handling large deformations,
fast up to 30 fps, free of training, and robust against partial occlusions and
discontinuity in video frames. It outperforms the state-of-the-art methods in
challenging datasets. ROBUSfT is implemented as a publicly available C++
library and we provide a tutorial on how to use it in
https://github.com/mrshetab/ROBUSfT
