Pyramidal Signed Distance Learning for Spatio-Temporal Human Shape Completion

We address the problem of completing partial human shape observations as obtained with a depth camera. Existing methods that solve this problem can provide robustness, with for instance model-based strategies that rely on parametric human models, or precision, with learning approaches that can capture local geometric patterns using implicit neural representations. We investigate how to combine both properties with a novel pyramidal spatio-temporal learning model. This model exploits neural signed distance fields in a coarse-to-fine manner, this in order to benefit from the ability of implicit neural representations to preserve local geometry details while enforcing more global spatial consistency for the estimated shapes through features at coarser levels. In addition, our model also leverages temporal redundancy with spatiotemporal features that integrate information over neighboring frames. Experiments on standard datasets show that both the coarse-to-fine and temporal aggregation strategies contribute to outperform the state-ofthe-art methods on human shape completion