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Human Shape from Silhouettes Using Generative HKS Descriptors and Cross-Modal Neural Networks

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Dibra, Endri 
Jain, Himanshu 
Oztireli, Cengiz 
Ziegler, Remo 
Gross, Markus 


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.



46 Information and Computing Sciences, 4607 Graphics, Augmented Reality and Games, 4611 Machine Learning, Bioengineering, Generic health relevance

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2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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