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Direct prediction of 3D body pose and shape remains a challenge even forhighly parameterized deep learning models. Mapping from the 2D image space tothe prediction space is difficult: perspective ambiguities make the lossfunction noisy and training data is scarce. In this paper, we propose a novelapproach (Neural Body Fitting (NBF)). It integrates a statistical body modelwithin a CNN, leveraging reliable bottom-up semantic body part segmentation androbust top-down body model constraints. NBF is fully differentiable and can betrained using 2D and 3D annotations. In detailed experiments, we analyze howthe components of our model affect performance, especially the use of partsegmentations as an explicit intermediate representation, and present a robust,efficiently trainable framework for 3D human pose estimation from 2D imageswith competitive results on standard benchmarks. Code will be made available athttp://github.com/mohomran/neural_body_fitting

Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation