SMPLR: Deep learning based SMPL reverse for 3D human pose and shape recovery

Highlights：

• We use sparse body surface landmarks to represent the body shape and disambiguate joints orientations.

• We train a deep neural network to estimate the body pose and a detailed mesh without any special constraints on the skinned model parameters.

• Estimating the detailed mesh from 3D locations of joints and sparse landmarks is more accurate than estimating them directly from RGB image.

• We propose an efficient incremental and end-to-end training where a part of the model is a regularizer to the other part.

• Our simple modifications to a volumetric stacked hourglass network along with the proposed end-to-end training shows significant improvement over state-of-the-art human 3D pose estimation.