Amazon Technologies, Inc.

Max-planck-gesellschaft Zur F&#xf6;rderung Der Wissenschaften E. V.

Xiaochen Hu

Matthew Maverick Loper

Michael Julian Black

David Allen Smith

Nicolas Heron

Eric Rachlin

Javier Romero Gonzalez-Nicolas

Alexander Weiss

Martin Kristiansen

Theodora Hinkle

A system configured to perform an accurate and fast estimation of an object shape from a single input image. The system may process image data representing a first surface of an object using image-to-image translation techniques. A first trained model may generate depth information for the object, such as front distance estimates and back distance estimates. The system may use the depth information to generate an output mesh shaped like the object, such as, in the case of a pliable object a reposable avatar. The system may improve depth estimation by including a loss on surface normals in the first trained model. A second trained model may generate color information to be applied to the output mesh to accurately represent the object. The output mesh may include detailed geometry and appearance of the object, useful for a variety of purposes such as gaming, virtual/augmented reality, virtual shopping, and other implementations.

Detailed 3D estimation from a single image

Disclosed is a method including receiving an input image including a human, predicting, based on a convolutional neural network that is trained using examples consisting of pairs of sensor data, a corresponding body shape of the human and utilizing the corresponding body shape predicted from the convolutional neural network as input to another convolutional neural network to predict additional body shape metrics.

Machine learning systems and methods of estimating body shape from images

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Jersey City, NJ, United States of America

Xiaochen Hu