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Abstract

We present a learning-based model to infer the personalized 3D shape of
people from a few frames (1-8) of a monocular video in which the person is
moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our
model learns to predict the parameters of a statistical body model and instance
displacements that add clothing and hair to the shape. The model achieves fast
and accurate predictions based on two key design choices. First, by predicting
shape in a canonical T-pose space, the network learns to encode the images of
the person into pose-invariant latent codes, where the information is fused.
Second, based on the observation that feed-forward predictions are fast but do
not always align with the input images, we predict using both, bottom-up and
top-down streams (one per view) allowing information to flow in both
directions. Learning relies only on synthetic 3D data. Once learned, the model
can take a variable number of frames as input, and is able to reconstruct
shapes even from a single image with an accuracy of 6mm. Results on 3 different
datasets demonstrate the efficacy and accuracy of our approach.