Incorporating Exemplar Optimization into Training with Dual Networks for
  Human Mesh Recovery
Yongwei Nie Mingxian Fan Chengjiang Long Qing Zhang Jian Zhu Xuemiao Xu
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.