  Cross view feature fusion is the key to address the occlusion problem in
human pose estimation. The current fusion methods need to train a separate
model for every pair of cameras making them difficult to scale. In this work,
we introduce MetaFuse, a pre-trained fusion model learned from a large number
of cameras in the Panoptic dataset. The model can be efficiently adapted or
finetuned for a new pair of cameras using a small number of labeled images. The
strong adaptation power of MetaFuse is due in large part to the proposed
factorization of the original fusion model into two parts (1) a generic fusion
model shared by all cameras, and (2) lightweight camera-dependent
transformations. Furthermore, the generic model is learned from many cameras by
a meta-learning style algorithm to maximize its adaptation capability to
various camera poses. We observe in experiments that MetaFuse finetuned on the
public datasets outperforms the state-of-the-arts by a large margin which
validates its value in practice.
