# Deep Non-rigid Structure-from-Motion Revisited: Canonicalization and Sequence Modeling

arXiv: https://arxiv.org/abs/2412.07230 · HTML: https://mlsanity.com/paper/2412.07230/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Deep Non-rigid Structure-from-Motion: A Sequence-to-Sequence Translation Perspective](https://mlsanity.com/paper/2204.04730/) (2022)

## Built on by (most cited first, 2 of 2)

- [Unsupervised 2D-3D lifting of non-rigid objects using local constraints](https://mlsanity.com/paper/2504.19227/) (2025)
- [Efficient and Robust Camera-independent Multiview 3D Geometric Reconstruction from Noisy Monocular Depth Estimation and Multiple Point Matching](https://mlsanity.com/paper/2609.05972/) (2026)

## Abstract (arXiv)

Non-Rigid Structure-from-Motion (NRSfM) is a classic 3D vision problem, where a 2D sequence is taken as input to estimate the corresponding 3D sequence. Recently, the deep neural networks have greatly advanced the task of NRSfM. However, existing deep NRSfM methods still have limitations in handling the inherent sequence property and motion ambiguity associated with the NRSfM problem. In this paper, we revisit deep NRSfM from two perspectives to address the limitations of current deep NRSfM methods : (1) canonicalization and (2) sequence modeling. We propose an easy-to-implement per-sequence canonicalization method as opposed to the previous per-dataset canonicalization approaches. With this in mind, we propose a sequence modeling method that combines temporal information and subspace constraint. As a result, we have achieved a more optimal NRSfM reconstruction pipeline compared to previous efforts. The effectiveness of our method is verified by testing the sequence-to-sequence deep NRSfM pipeline with corresponding regularization modules on several commonly used datasets.