Computer Graphics Laboratory ETH Zurich


Shape from Selfies : Human Body Shape Estimation using CCA Regression Forests

E. Dibra, C. Öztireli, R. Ziegler, M. Gross

Proceedings of Computer Vision - (ECCV) - 14th European Conference (Amsterdam, the Netherlands, October 8-16, 2016), pp. 88-104


In this work, we revise the problem of human body shape estimation from monocular imagery. Starting from a statistical human shape model that describes a body shape with shape parameters, we describe a novel approach to automatically estimate these parameters from a single input shape silhouette using semi-supervised learning. By utilizing silhouette features that encode local and global properties robust to noise, pose and view changes, and projecting them to lower dimensional spaces obtained through multi-view learning with canonical correlation analysis, we show how regression forests can be used to compute an accurate mapping from the silhouette to the shape parameter space. This results in a very fast, robust and automatic system under mild self-occlusion assumptions. We extensively evaluate our method on thousands of synthetic and real data and compare it to the state-of-art approaches that operate under more restrictive assumptions.


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