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We present the first method for real-time full body capture that estimatesshape and motion of body and hands together with a dynamic 3D face model from asingle color image. Our approach uses a new neural network architecture thatexploits correlations between body and hands at high computational efficiency.Unlike previous works, our approach is jointly trained on multiple datasetsfocusing on hand, body or face separately, without requiring data where all theparts are annotated at the same time, which is much more difficult to create atsufficient variety. The possibility of such multi-dataset training enablessuperior generalization ability. In contrast to earlier monocular full bodymethods, our approach captures more expressive 3D face geometry and color byestimating the shape, expression, albedo and illumination parameters of astatistical face model. Our method achieves competitive accuracy on publicbenchmarks, while being significantly faster and providing more complete facereconstructions.

Monocular Real-time Full Body Capture with Inter-part Correlations