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Human Shape from Silhouettes Using Generative HKS Descriptors and Cross-Modal Neural Networks
In this work, we present a novel method for capturing
human body shape from a single scaled silhouette. We combine deep correlated features capturing different 2D views,
and embedding spaces based on 3D cues in a novel convolutional neural network (CNN) based architecture. We
first train a CNN to find a richer body shape representation space from pose invariant 3D human shape descriptors.
Then, we learn a mapping from silhouettes to this representation space, with the help of a novel architecture that exploits the correlation of multi-view data during training time, to
improve prediction at test time. We extensively validate our
results on synthetic and real data, demonstrating significant
improvements in accuracy as compared to the state-of-the-art, and providing a practical system for detailed human
body measurements from a single image