TY  - JOUR
TI  - SMPL and SMPL-X parametric body models
AU  - Loper, M.
AU  - Mahmood, N.
AU  - Romero, J.
AU  - Pons-Moll, G.
AU  - Black, M. J.
PY  - 2015
DA  - 2015 / 2019///
JO  - ACM Transactions on Graphics (SIGGRAPH Asia 2015) / CVPR 2019
AB  - Loper et al. (2015) introduced SMPL, a parametric body model that represents a human figure as a blend of shape and pose parameters derived from a large database of body scans; the model outputs a posed mesh via learned blend shapes and skinning weights. Pavlakos et al. (2019) extended it to SMPL-X, adding articulated hands and an expressive face model for whole-body capture. Together they form the de facto output representation for learning-based human pose estimation, motion capture and text-to-motion generation: the Motion Diffusion Model, DeepPhase and most motion-synthesis methods produce SMPL parameters, making the model prerequisite knowledge for reading their outputs and results in animation research and teaching.
KW  - motion-synthesis
KW  - character-animation
UR  - https://dl.acm.org/doi/10.1145/2816795.2818013
LA  - en
ER  -