Human Body Shape Estimation Using a Multi-Resolution Manifold Forest
This paper proposes a method for estimating the 3D body shape of a person with robustness to clothing. We formulate the problem as optimization over the manifold of valid depth maps of body shapes learned from synthetic training data. The manifold itself is represented using a novel data structure, a Multi-Resolution Manifold Forest (MRMF), which contains vertical edges between tree nodes as well as horizontal edges between nodes across trees that correspond to overlapping partitions. We show that this data structure allows both efficient localization and navigation on the manifold for on-the-fly building of local linear models (manifold charting). We demonstrate shape estimation of clothed users, showing significant improvement in accuracy over global shape models and models using pre-computed clusters. We further compare the MRMF with alternative manifold charting methods on a public dataset for estimating 3D motion from noisy 2D marker observations, obtaining state-of-the-art results.
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