PNO: Personalized Network Optimization for Human Pose and Shape Reconstruction

Most previous human pose and shape reconstruction methods focus on the generalization ability and learn a prior of the general pose and shape, however the personalized features are often ignored. We argue that the personalized features such as appearance and body shape are always consistent for the specific person and can further improve the accuracy. In this paper, we propose a Personalized Network Optimization (PNO) method to maintain both generalization and personality for human pose and shape reconstruction. The general trained network is adapted to the personalized network by optimizing with only a few unlabeled video frames of the target person. Moreover, we specially propose geometry-aware temporal constraints that help the network better exploit the geometry knowledge of the target person. In order to prove the effectiveness of PNO, we re-design the benchmark of pose and shape reconstruction to test on each person independently. Experiments show that our method achieve the state-of-the-art results in both 3DPW and MPI-INF-3DHP datasets.