  Estimating 3D human pose and shape from a single image is highly
under-constrained. To address this ambiguity, we propose a novel prior, namely
kinematic dictionary, which explicitly regularizes the solution space of
relative 3D rotations of human joints in the kinematic tree. Integrated with a
statistical human model and a deep neural network, our method achieves
end-to-end 3D reconstruction without the need of using any shape annotations
during the training of neural networks. The kinematic dictionary bridges the
gap between in-the-wild images and 3D datasets, and thus facilitates end-to-end
training across all types of datasets. The proposed method achieves competitive
results on large-scale datasets including Human3.6M, MPI-INF-3DHP, and LSP,
while running in real-time given the human bounding boxes.
