  We tackle the problem of single-image Human Mesh Recovery (HMR). Previous
approaches are mostly based on a single crop. In this paper, we shift the
single-crop HMR to a novel multiple-crop HMR paradigm. Cropping a human from
image multiple times by shifting and scaling the original bounding box is
feasible in practice, easy to implement, and incurs neglectable cost, but
immediately enriches available visual details. With multiple crops as input, we
manage to leverage the relation among these crops to extract discriminative
features and reduce camera ambiguity. Specifically, (1) we incorporate a
contrastive learning scheme to enhance the similarity between features
extracted from crops of the same human. (2) We also propose a crop-aware fusion
scheme to fuse the features of multiple crops for regressing the target mesh.
(3) We compute local cameras for all the input crops and build a
camera-consistency loss between the local cameras, which reward us with less
ambiguous cameras. Based on the above innovations, our proposed method
outperforms previous approaches as demonstrated by the extensive experiments.
