Name
Abstract | ||
|---|---|---|
Most 3D human mesh regressors are fully supervised with 3D pseudo-GT human model parameters and weakly supervised with GT 2D/3D joint coordinates as the 3D pseudo-GTs bring great performance gain. The 3D pseudo-GTs are obtained by annotators, systems that iteratively fit 3D human model parameters to GT 2D/3D joint coordinates of training sets in the pre-processing stage of the regressors. The fitted 3D parameters at the last fitting iteration become the 3D pseudo-GTs, used to fully super-vise the regressors. Optimization-based annotators, such as SMPLify-X, have been widely used to obtain the 3D pseudo-GTs. However, they often produce wrong 3D pseudo-GTs as they fit the 3D parameters to GT of each sample independently. To overcome the limitation, we present NeuralAnnot, a neural network-based annotator. The main idea of NeuralAnnot is to employ a neural network-based regressor and dedicate it for the annotation. Assuming no 3D pseudo-GTs are available, NeuralAnnot is weakly supervised with GT 2D/3D joint coordinates of training sets. The testing results on the same training sets become 3D pseudo-GTs, used to fully supervise the regressors. We show that 3D pseudo-GTs of NeuralAnnot are highly beneficial to train the regressors. We made our 3D pseudo-GTs publicly available. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPRW56347.2022.00256 | IEEE Conference on Computer Vision and Pattern Recognition |
DocType | Volume | Issue |
Conference | 2022 | 1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gyeongsik Moon | 1 | 0 | 0.34 |
| Hongsuk Choi | 2 | 1 | 1.70 |
| Kyoung Mu Lee | 3 | 3228 | 153.84 |