Abstract. We present a convolutional autoencoder that enables high
fidelity volumetric reconstructions of human performance to be captured
from multi-view video comprising only a small set of camera views. Our
method yields similar end-to-end reconstruction error to that of a probabilistic visual hull computed using significantly more (double or more)
viewpoints. We use a deep prior implicitly learned by the autoencoder
trained over a dataset of view-ablated multi-view video footage of a wide
range of subjects and actions. This opens up the possibility of high-end
volumetric performance capture in on-set and prosumer scenarios where
time or cost prohibit a high witness camera count