MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views

• URL: http://arxiv.org/abs/2207.09086v1
• Date: Tue, 19 Jul 2022 05:47:03 GMT
• Title: MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views
• Authors: Haitian Zeng, Xin Yu, Jiaxu Miao, Yi Yang
• Abstract summary: We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from Motion (NRSfM) MHR-Net aims to find a set of reasonable reconstructions for a 2D view, and it also selects the most likely reconstruction from the set. Experiments show that MHR-Net achieves state-of-the-art reconstruction accuracy on Human3.6M, SURREAL and 300-VW datasets.
• Score: 46.022341180146206
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from Motion (NRSfM). MHR-Net aims to find a set of reasonable reconstructions for a 2D view, and it also selects the most likely reconstruction from the set. To deal with the challenging unsupervised generation of non-rigid shapes, we develop a new Deterministic Basis and Stochastic Deformation scheme in MHR-Net. The non-rigid shape is first expressed as the sum of a coarse shape basis and a flexible shape deformation, then multiple hypotheses are generated with uncertainty modeling of the deformation part. MHR-Net is optimized with reprojection loss on the basis and the best hypothesis. Furthermore, we design a new Procrustean Residual Loss, which reduces the rigid rotations between similar shapes and further improves the performance. Experiments show that MHR-Net achieves state-of-the-art reconstruction accuracy on Human3.6M, SURREAL and 300-VW datasets.

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