MPI FAUST Dataset

Results and Rankings

Results for methods appear here after users upload them and approve them for public display.


Intra-subject challenge
Average error
SP_intra_challenge [1] 1.568 Visualize Results
Convex-Opt [2] 4.86 Visualize Results
CHARM [3] 15.157 Visualize Results
FMNet [4] 2.436 Visualize Results
FARM [5] 2.81 Visualize Results
3D-CODED : 3D Correspondences by Deep Deformation [6] 1.985 Visualize Results
LBS-AE (Unsupervised) [7] 2.161 Visualize Results
Self-supervised learning [8] 2.51 Visualize Results
unsupervised 3D-CODED : 3D Correspondences by Deep Deformation [9] 2.789 Visualize Results
BPS [10] 2.327 Visualize Results
References
[1]
SP_intra_challenge
S. Zuffi, M. J. Black, "The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose", CVPR, Boston, MA, June 2015.
[2]
Convex-Opt
Robust Nonrigid Registration by Convex Optimization. Qifeng Chen, Vladlen Koltun. International Conference on Computer Vision (ICCV), 2015
[3]
CHARM
[4]
FMNet
"Deep functional maps: Structured prediction for dense shape correspondence". Litany, Remez, Rodola, Bronstein, Bronstein. Proc. ICCV 2017
[5]
FARM
"FARM: Functional Automatic Registration Method for 3D Human Bodies". Marin, Melzi, Rodola, Castellani. arXiv:1807.10517, 2018.
[6]
3D-CODED : 3D Correspondences by Deep Deformation
3D-CODED : 3D Correspondences by Deep Deformation, Groueix, Thibault and Fisher, Matthew and Kim, Vladimir G. and Russell, Bryan and Aubry, Mathieu, ECCV 2018.
[7]
LBS-AE (Unsupervised)
LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds, CVPR 2019
[8]
Self-supervised learning
Self-supervised Learning of Dense Shape Correspondence
[9]
unsupervised 3D-CODED : 3D Correspondences by Deep Deformation
3D-CODED : 3D Correspondences by Deep Deformation, Groueix, Thibault and Fisher, Matthew and Kim, Vladimir G. and Russell, Bryan and Aubry, Mathieu, ECCV 2018.
[10]
BPS
Anonymous.