Name
Abstract | ||
|---|---|---|
Deep learning methods for 3D human pose estimation from RGB images require a huge amount of domain-specific labeled data for good in-the-wild performance. However, obtaining annotated 3D pose data requires a complex motion capture setup which is generally limited to controlled settings. We propose a semi-supervised learning method using a structure-aware loss function which is able to utilize abundant 2D data to learn 3D information. Furthermore, we present a simple temporal network which uses additional context present in pose sequences to improve and temporally harmonize the pose estimates. Our complete pipeline improves upon the state-of-the-art by 11.8% and works at 30 FPS on a commodity graphics card. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Graphics,Computer vision,Motion capture,Computer science,Pose,RGB color model,Artificial intelligence,Labeled data,Deep learning |
DocType | Volume | Citations |
Journal | abs/1711.09250 | 1 |
PageRank | References | Authors |
0.35 | 21 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rishabh Dabral | 1 | 5 | 3.11 |
| Anurag Mundhada | 2 | 1 | 0.35 |
| Uday Kusupati | 3 | 1 | 0.35 |
| Safeer Afaque | 4 | 1 | 0.35 |
| Arjun Jain | 5 | 673 | 29.40 |