20 papers with code • 3 benchmarks • 2 datasets
Pose prediction is to predict future poses given a window of previous poses.
HigherHRNet even surpasses all top-down methods on CrowdPose test (67. 6% AP), suggesting its robustness in crowded scene.
Ranked #2 on Multi-Person Pose Estimation on CrowdPose
For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches when they are all used without post-processing.
Ranked #1 on 6D Pose Estimation using RGB on OCCLUSION
A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding.
We validate AS-GCN in action recognition using two skeleton data sets, NTU-RGB+D and Kinetics.
We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure.
The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm.
Ranked #3 on 6D Pose Estimation using RGB on YCB-Video
We show how most tracking tasks can be solved within this framework, and that the same appearance model can be used to obtain performance that is competitive against specialised methods for all the five tasks considered.
Ranked #2 on Pose Tracking on PoseTrack2018
Thus, we propose STRAPS (Synthetic Training for Real Accurate Pose and Shape), a system that utilises proxy representations, such as silhouettes and 2D joints, as inputs to a shape and pose regression neural network, which is trained with synthetic training data (generated on-the-fly during training using the SMPL statistical body model) to overcome data scarcity.
Ranked #15 on 3D Human Pose Estimation on 3DPW