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While dense non-rigid structure from motion (NRSfM) has been extensivelystudied from the perspective of the reconstructability problem over the recentyears, almost no attempts have been undertaken to bring it into the practicalrealm. The reasons for the slow dissemination are the severe ill-posedness,high sensitivity to motion and deformation cues and the difficulty to obtainreliable point tracks in the vast majority of practical scenarios. To fill thisgap, we propose a hybrid approach that extracts prior shape knowledge from aninput sequence with NRSfM and uses it as a dynamic shape prior for sequentialsurface recovery in scenarios with recurrence. Our Dynamic Shape PriorReconstruction (DSPR) method can be combined with existing dense NRSfMtechniques while its energy functional is optimised with stochastic gradientdescent at real-time rates for new incoming point tracks. The proposedversatile framework with a new core NRSfM approach outperforms several othermethods in the ability to handle inaccurate and noisy point tracks, provided wehave access to a representative (in terms of the deformation variety) imagesequence. Comprehensive experiments highlight convergence properties and theaccuracy of DSPR under different disturbing effects. We also perform a jointstudy of tracking and reconstruction and show applications to shape compressionand heart reconstruction under occlusions. We achieve state-of-the-art metrics(accuracy and compression ratios) in different scenarios.

Intrinsic Dynamic Shape Prior for Fast, Sequential and Dense Non-Rigid Structure from Motion with Detection of Temporally-Disjoint Rigidity