This paper advocates the use of organic priors in classical non-rigid
structure from motion (NRSfM). By organic priors, we mean invaluable
intermediate prior information intrinsic to the NRSfM matrix factorization
theory. It is shown that such priors reside in the factorized matrices, and
quite surprisingly, existing methods generally disregard them. The paper's main
contribution is to put forward a simple, methodical, and practical method that
can effectively exploit such organic priors to solve NRSfM. The proposed method
does not make assumptions other than the popular one on the low-rank shape and
offers a reliable solution to NRSfM under orthographic projection. Our work
reveals that the accessibility of organic priors is independent of the camera
motion and shape deformation type. Besides that, the paper provides insights
into the NRSfM factorization -- both in terms of shape and motion -- and is the
first approach to show the benefit of single rotation averaging for NRSfM.
Furthermore, we outline how to effectively recover motion and non-rigid 3D
shape using the proposed organic prior based approach and demonstrate results
that outperform prior-free NRSfM performance by a significant margin. Finally,
we present the benefits of our method via extensive experiments and evaluations
on several benchmark datasets.Comment: To appear in ECCV 2022 Conference (Oral Presentation). Draft info: 18
Pages, 4 Figures, and 6 Tables. Project webpage:
https://suryanshkumar.github.io/Organic_Prior_NRSfM
