Over the past few years, differentiable parametric models such as SMPL  for human bodies or MANO  for human hands have lead to significant progress in body/hand shape estimation from single images. In particular, several methods [3,4] leverage CNNs to directly regress SMPL/MANO parameters. These parametric models allow to control the shape by splitting the paremeters into camera pose parameters, shape parameters and pose parameters.
However, this expressive and meaningful space of the parameters might suffer from their design as some variables have significantly different statistics and impact on the output compared to some others. The goal of this internship is to learn a better space for direct regression of parametric models by CNNs. Further application of such parametric model embeddings could include human shape dynamic prediction .
Supervisor: Philippe Weinzaepfel
 SMPL: A skinned multi-person linear model. Loper et al. ACM Trans. Graphics 2015.
 Embodied Hands: Modeling and capturing hands and bodies together. Romero et al. ACM
Trans. Graphics 2017.
 Learning to reconstruct 3D human pose and shape via model-fitting in the loop. Pavlakos et
al. ICCV 2019.
 Delving Deep Into Hybrid Annotations for 3D Human Recovery in the Wild. Rong et al. ICCV
 Predicting 3D human dynamics from video. Zhang et al. ICCV 2019.
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