  The availability of affordable 3D full body reconstruction systems has given
rise to free-viewpoint video (FVV) of human shapes. Most existing solutions
produce temporally uncorrelated point clouds or meshes with unknown
point/vertex correspondences. Individually compressing each frame is
ineffective and still yields to ultra-large data sizes. We present an
end-to-end deep learning scheme to establish dense shape correspondences and
subsequently compress the data. Our approach uses sparse set of "panoramic"
depth maps or PDMs, each emulating an inward-viewing concentric mosaics. We
then develop a learning-based technique to learn pixel-wise feature descriptors
on PDMs. The results are fed into an autoencoder-based network for compression.
Comprehensive experiments demonstrate our solution is robust and effective on
both public and our newly captured datasets.
