mlsanityData to 7 Oct 2026

Deep Non-rigid Structure-from-Motion: A Sequence-to-Sequence Translation Perspective

arXiv 2204.04730 ↗First posted 10 Apr 2022cs.CVcited by 8

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Abstract (arXiv)

Directly regressing the non-rigid shape and camera pose from the individual 2D frame is ill-suited to the Non-Rigid Structure-from-Motion (NRSfM) problem. This frame-by-frame 3D reconstruction pipeline overlooks the inherent spatial-temporal nature of NRSfM, i.e., reconstructing the whole 3D sequence from the input 2D sequence. In this paper, we propose to model deep NRSfM from a sequence-to-sequence translation perspective, where the input 2D frame sequence is taken as a whole to reconstruct the deforming 3D non-rigid shape sequence. First, we apply a shape-motion predictor to estimate the initial non-rigid shape and camera motion from a single frame. Then we propose a context modeling module to model camera motions and complex non-rigid shapes. To tackle the difficulty in enforcing the global structure constraint within the deep framework, we propose to impose the union-of-subspace structure by replacing the self-expressiveness layer with multi-head attention and delayed regularizers, which enables end-to-end batch-wise training. Experimental results across different datasets such as Human3.6M, CMU Mocap and InterHand prove the superiority of our framework.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 31 of its 69 references that are in the map. First posted on arXiv 10 Apr 2022.

Builds on

31 of 69 references.

References · 31

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19 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 3; 2026: 1.

8 papers.