论文标题
MHR-NET：从2D视图中对非刚性形状的多种假设重建
MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views
论文作者
论文摘要
我们提出了MHR-NET，这是一种从运动（NRSFM）中恢复非刚性形状的新方法。 MHR-NET旨在为2D视图找到一组合理的重建，并且还从集合中选择了最有可能的重建。为了应对具有挑战性的无刚性形状，我们在MHR-NET中开发了新的确定性基础和随机变形方案。非刚性形状首先表示为粗大基的总和和柔性形状变形，然后以变形部分的不确定性建模产生多个假设。 MHR-NET通过基础和最佳假设进行了重新投入损失进行了优化。此外，我们设计了一种新的procrustean残差损失，从而降低了相似形状之间的刚性旋转并进一步改善了性能。实验表明，MHR-NET可以在36M，超现实和300-VW数据集上实现最新的重建精度。
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.
