SPIN: Human pose and shape from an image
2019-10-01
SPIN is a state-of-the-art deep network for regressing SMPL body shape and pose parameters directly from an image. SPIN uses a novel training method that combines a bottom-up deep network with a top-down, model-based, fitting method. SMPLify model fitting is used in the loop with the DNN training to provide SMPL parameters used in the training loss. Code is available.
SPIN is a state-of-the-art deep network for regressing SMPL body shape and pose parameters directly from an image. SPIN uses a novel training method that combines a bottom-up deep network with a top-down, model-based, fitting method. SMPLify model fitting is used in the loop with the DNN training to provide SMPL parameters used in the training loss. Code is available.
| Author(s): | Kolotouros, Nikos and Pavlakos, Georgios and Black, Michael J. and Daniilidis, Kostas |
| Department(s): |
Perceiving Systems |
| Publication(s): |
Learning to Reconstruct {3D} Human Pose and Shape via Model-fitting in the Loop
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| Authors: | Kolotouros, Nikos and Pavlakos, Georgios and Black, Michael J. and Daniilidis, Kostas |
| Release Date: | 2019-10-01 |
| Repository: | https://github.com/nkolot/SPIN |
