We propose a novel optimization-based human mesh recovery method from a single image. Given a test exemplar, previous approaches optimize the pre-trained regression network to minimize the 2D re-projection loss, which however suffer from over-/under-fitting problems. This is because the ``exemplar optimization'' at testing time has too weak relation to the pre-training process, and the exemplar optimization loss function is different from the training loss function. (1) We incorporate exemplar optimization into the training stage. During training, our method first executes exemplar optimization and subsequently proceeds with training-time optimization. The exemplar optimization may run into a wrong direction, while the subsequent training optimization serves to correct the deviation. Involved in training, the exemplar optimization learns to adapt its behavior to training data, thereby acquires generalibility to test exemplars. (2) We devise a dual-network architecture to convey the novel training paradigm, which is composed of a main regression network and an auxiliary network, in which we can formulate the exemplar optimization loss function in the same form as the training loss function. This further enhances the compatibility between the exemplar and training optimizations. Experiments demonstrate that our exemplar optimization after the novel training scheme significantly outperforms state-of-the-art approaches.

翻译：我们提出一种基于优化的新型单张图像人体网格重建方法。给定测试示例时，现有方法通过优化预训练回归网络以最小化二维重投影损失，但这会导致过拟合/欠拟合问题。其根本原因在于：测试阶段的"示例优化"与预训练过程的关联性过弱，且示例优化损失函数与训练损失函数存在差异。(1)我们将示例优化融入训练阶段。在训练过程中，我们的方法首先执行示例优化，随后进行训练时优化。示例优化可能偏离正确方向，而后续的训练优化可纠正该偏差。通过参与训练，示例优化能够自适应调整行为适配训练数据，从而获得对测试示例的泛化能力。(2)我们设计了一种双网络架构来实现这一新型训练范式，该架构由主回归网络和辅助网络组成，使得示例优化损失函数与训练损失函数可采用相同形式表达。这进一步增强了示例优化与训练优化的兼容性。实验结果表明，采用新型训练方案后的示例优化方法显著优于现有最佳方法。